U.S. patent application number 13/772527 was filed with the patent office on 2013-10-17 for methods and systems for integrated health systems.
The applicant listed for this patent is Soar BioDynamics, Ltd.. Invention is credited to Thomas NEVILLE.
Application Number | 20130275050 13/772527 |
Document ID | / |
Family ID | 41398768 |
Filed Date | 2013-10-17 |
United States Patent
Application |
20130275050 |
Kind Code |
A1 |
NEVILLE; Thomas |
October 17, 2013 |
METHODS AND SYSTEMS FOR INTEGRATED HEALTH SYSTEMS
Abstract
Methods, business methods, and systems are provided herein for
integrated healthcare. As the amount of medical information
increases rapidly, including information from multiple biomarkers,
analysis and management of that information becomes more and more
important to extract meaningful conclusions from the information.
Statistical and computational methods are described herein that
have been created for the methods and systems for integrated
healthcare. For example, a computer system is described extracts
significance over time of PSA and fPSA biomarker tests for prostate
health.
Inventors: |
NEVILLE; Thomas; (Incline
Village, NV) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Soar BioDynamics, Ltd. |
Incline Village |
NV |
US |
|
|
Family ID: |
41398768 |
Appl. No.: |
13/772527 |
Filed: |
February 21, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12466684 |
May 15, 2009 |
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13772527 |
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61053600 |
May 15, 2008 |
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Current U.S.
Class: |
702/19 |
Current CPC
Class: |
G06F 19/00 20130101;
G16H 50/20 20180101; G16H 50/30 20180101; G16H 50/70 20180101 |
Class at
Publication: |
702/19 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1-63. (canceled)
64. A method for calculating a probability of prostate cancer in a
subject, comprising: obtaining a series of at least two PSA values
from a subject; using a computer system to calculate a
characteristic of said series of PSA values, wherein said
characteristic is PSA variation; measuring a prostate volume in
said subject; comparing said PSA variation and said prostate volume
to population data; and calculating a probability of prostate
cancer in said subject based on the comparison.
65. The method of claim 64, wherein the step of using a computer
system to calculate a characteristic of said series of PSA values
comprises: using a computer system to fit said series of PSA values
to form a fitted trend; and calculating said characteristic of said
fitted trend, wherein said characteristic reflects variation of
said series of PSA values from said fitted trend.
66. The method of claim 64, wherein the population data is stored
in a computer-readable medium.
67. The method of claim 64, wherein the step of calculating a
probability of prostate cancer in the subject is performed by a
computer system.
68. The method of claim 64, further comprising obtaining a series
of values of a prostate biomarker from said subject, wherein
calculating a probability of prostate cancer is further based on
said series of values of said prostate biomarker.
69. The method of claim 68, wherein said prostate biomarker is free
PSA.
70. The method of claim 64, wherein measuring a prostate volume in
said subject comprises measuring prostate volume in said subject at
least two times to obtain a series of prostate volumes and
calculating a prostate volume growth of said subject; and wherein
comparing said prostate volume to population data comprises
comparing said prostate volume growth to population data.
71. The method of claim 64, further comprising calculating a
recommended time for medical action, wherein said medical action is
biopsy or medical treatment.
72. The method of claim 64, wherein said population data comprises
pre-calculated frequency distributions of PSA variation.
73. The method of claim 64, wherein said population data comprises
pre-calculated frequency distributions of prostate volume.
74. A method for calculating a probability of death due to prostate
cancer by a subject, comprising: obtaining a series of at least two
PSA values from a subject; using a computer system to calculate a
characteristic of said series of PSA values, wherein said
characteristic is PSA variation; measuring a prostate volume in
said subject; comparing said PSA variation and said prostate volume
to population data; and calculating a probability of death due to
prostate cancer by said subject based on the comparison.
75. The method of claim 74, wherein the step of using a computer
system to calculate a characteristic of said series of PSA values
comprises: using a computer system to fit said series of PSA values
to form a fitted trend; and calculating a characteristic of said
fitted trend, wherein said characteristic reflects variation of
said series of PSA values from said fitted trend.
76. The method of claim 74, wherein calculating a probability of
death due to prostate cancer by said subject comprises calculating
a probability of prostate cancer in said subject.
77. The method of claim 74, further comprising calculating a
recommended time for a medical action, wherein said medical action
is biopsy or a medical treatment.
78. The method of claim 77, wherein calculating a recommended time
for a medical action comprises comparing said probability of death
due to prostate cancer with a risk of side effects of said medical
action.
79. A computer system for calculating a probability of prostate
cancer in a subject, comprising: a processor suitable for
performing computer-readable instructions for: receiving a series
of PSA values measured in a subject; receiving a prostate volume
measured in said subject; calculating a characteristic of said
series of PSA values, wherein said characteristic is PSA variation;
comparing said PSA variation and said prostate volume to population
data; and calculating a probability of prostate cancer in said
subject based on the comparison; a storage unit comprising said
population data in communication with said processor; and an output
device suitable for transmitting the results of said
computer-readable instructions to a user.
80. The computer system of claim 79, wherein calculating a
characteristic of said series of PSA values comprises: fitting said
series of PSA values to form a fitted trend; and calculating a
characteristic of said fitted trend, wherein said characteristic
reflects variation of said series of PSA values from said fitted
trend.
81. The computer system of claim 79, wherein said processor is
further suitable for performing computer-readable instructions for
calculating a probability of death due to prostate cancer by said
subject.
82. The computer system of claim 79, wherein said output device is
a device for network communication.
83. The computer system of claim 79, further comprising an input
device for receiving data taken from said subject.
Description
CROSS-REFERENCE
[0001] This application is a continuation application of U.S. Ser.
No. 12/466,684, filed on May 15, 2009 which claims the benefit of
U.S. Provisional Application No. 61/053,600, filed May 15, 2008,
which application is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] There is increasing emphasis on disease prevention, early
detection and treatment, avoiding unnecessary treatment, timing of
treatments, avoiding invasive procedures, and reducing costs.
Significant investments are being made to accelerate discovery and
use of biomarkers that effectively detect progressing cancer.
However, assaying or testing for an individual biomarker is often
not effective for detection of progressing cancer.
[0003] The use of screening blood tests, where multiple markers are
tested, is becoming more prevalent and cost-effective. Screening
for many conditions using blood from a single draw can reduce
medical costs. The incremental cost of additional tests decreases
if subsequent blood draws are not needed. A further means of
reducing costs is to store blood for later testing if needed. New
technology is also reducing the cost of specific tests.
[0004] There is a need in the art to extract additional information
from a diagnostic test, whether it is a biomarker test or a series
of biomarker tests, or a medical image. Novel methods and systems
of extracting additional quantitative information for use by
patients or physicians are increasingly desirable to reduce the
cost of medical diagnostics and treatments and to improve the
accuracy of diagnosis and efficacy of treatments.
SUMMARY OF THE INVENTION
[0005] In an aspect, a method is disclosed of performing a course
of medical action for a medical condition of a subject comprising:
obtaining a first value of at least one biomarker from a subject;
sending said first value to a computer system that calculates a
first plurality of posterior probabilities of the occurrence of a
plurality of medical conditions of said subject using said first
value, wherein said plurality of medical conditions comprises at
least a first and second medical condition; receiving said first
plurality of posterior probabilities; performing a first course of
medical action for the first medical condition based on said first
plurality of posterior probabilities; observing a result of said
first course of medical action; obtaining a second value of at
least one biomarker from said subject; sending said second value
and said result of said first course of the medical action to said
computer system that calculates a second plurality of posterior
probabilities of the occurrence of said plurality of medical
conditions of said subject, wherein said calculation uses said at
second value and said result; receiving said second plurality of
posterior probabilities; and performing a second course of medical
action for the second medical condition based on said second
plurality of posterior probabilities.
[0006] In an embodiment, the first or second value is a PSA value
or fPSA value. In another embodiment, the subject is a human, for
example a patient.
[0007] In an embodiment, a computer system comprises a device for
network communication, a storage unit, and a processor. The
computer system can comprise a Monte Carlo engine.
[0008] In an embodiment, sending comprises entering said first and
second values into a webpage or using a device that transmits
either or both of said first and second values to said computer
system through a wireless network.
[0009] In an embodiment, first and second values are a first and
second biomarker trend of biomarker values over a period of time. A
computer system can calculate each of said first plurality of
posterior probabilities by relating: a prior probability of a
medical condition; a probability of observing said first biomarker
trend for an individual with said medical condition; and a
probability of observing said first biomarker trend for an
individual without said medical condition. A computer system can
calculate each of said second plurality of posterior probabilities
by relating: a prior probability of a medical condition, wherein
said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability
of observing said second biomarker trend for an individual with
said medical condition; and a probability of observing said second
biomarker trend for an individual without said medical
condition.
[0010] In an embodiment, a plurality of medical conditions are
prostate medical conditions, for example they can be selected from
the group consisting of the following: prostatitis due to
inflammation, prostatitis due to infection, prostate cancer, benign
prostate hyperplasia, and no prostate disease.
[0011] In an embodiment, receiving comprises viewing a display of
said posterior probabilities, for example a display on an output
device. An output device can be selected from the group consisting
of the following: a computer, a webpage, an electronic medical
record, a printout, and a personal electronic device.
[0012] In an embodiment, a first or second course of medical action
is delivering medical treatment to said subject, such as a medical
treatment is selected from a group consisting of the following: a
pharmaceutical, surgery, organ resection, and radiation therapy. In
an embodiment, a pharmaceutical comprises a chemotherapeutic
compound for cancer therapy. In another embodiment, the first or
second course of medical action comprises administration of medical
tests or medical imaging of said subject or setting a specific time
for delivering medical treatment or a biopsy or a consultation with
a medical professional.
[0013] In another aspect, a business method is disclosed that
comprises: receiving a first value of at least one biomarker of a
subject; calculating a first plurality of posterior probabilities
of the occurrence of a plurality of medical conditions of said
subject with a computer system using said a first value; delivering
said first plurality of posterior probabilities to a user;
receiving a second value of at least one biomarker of a subject and
a result of a course of medical action taken by said user based
upon said delivery of said first plurality of posterior
probabilities; calculating a second plurality of posterior
probabilities of the occurrence of a plurality of medical
conditions of said subject with said computer system using said a
second value and said result of a course of the medical action; and
delivering said second plurality of posterior probabilities to said
user. In an embodiment, the first or second values are received
from a user, such as a user selected from the group consisting of
the following: a physician, a health care provider, a pharmacy, an
insurance company, and the subject. A first or second value can
also be received from said user through a webpage or an electronic
device or an assay device.
[0014] In another embodiment, the first or second values are
received from a device, such as a device selected from the group
consisting of the following: a lab test device, a point-of-care
assay device, a personal electronic device, an electronic medical
record, and a computer system.
[0015] Calculating can be carried out by a Monte Carlo engine and
can be a Bayesian statistical calculation.
[0016] In an embodiment, a plurality of medical conditions is at
least four medical conditions, for example from the group
consisting of: prostatitis due to inflammation, prostatitis due to
infection, prostate cancer, benign prostate hyperplasia, and no
prostate disease. A biomarker value can be from a PSA or fPSA
assay.
[0017] A result of a course of medical action can be selected from
the group consisting of the following: a test result, a diagnosis,
a cure, an effect, and no effect. Posterior probabilities can be
delivered to a user through an electronic medical record or a
webpage or an electronic device with a display or a printout.
[0018] In an embodiment, the computer system comprises a processor,
a storage unit, and a device for network communication.
[0019] In an embodiment, a business method is carried out for a
fee, for example each delivery of posterior probabilities is
carried out for a fee.
[0020] A business method can further comprise suggesting a course
of medical action to said user based on said posterior
probabilities, and the suggestion can be provided for a fee.
[0021] In an aspect of the invention, a method of delivering a
probability that a subject has a medical condition to a user
comprises: calculating a plurality of posterior probabilities of
the occurrence of a plurality of medical conditions of a subject
having a biomarker trend, wherein said biomarker trend comprises
biomarker values from said subject at more than one time, and
wherein each of said plurality of posterior probabilities is
calculated by relating: a prior probability of the occurrence of
each of said plurality of medical conditions; and a probability of
observing said biomarker trend for an individual with each medical
condition; and a probability of observing said biomarker trend for
an individual without each medical condition; and delivering said
plurality of probabilities of said plurality of medical conditions
to a user with an output device.
[0022] In another aspect, a method of delivering a probability that
a subject has a medical condition to a user comprises: calculating
a plurality of posterior probabilities of the occurrence of a
plurality of medical conditions of a subject having a result of a
course of medical action and having a biomarker trend, wherein said
biomarker trend comprises biomarker values from said subject at
more than one time, and wherein each of said plurality of posterior
probabilities is calculated by relating: a prior probability of the
occurrence of each of said plurality of medical conditions, wherein
said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability
of said biomarker trend for an individual with each medical
condition; and a probability of said biomarker trend for an
individual without each medical condition; and delivering said
plurality of probabilities of said plurality of medical conditions
to a user with an output device. A biomarker trend can be a PSA
trend or fPSA trend.
[0023] In an embodiment, an output device is selected from the
group consisting of the following: a computer, a webpage, an
electronic medical record, a printout, and a personal electronic
device.
[0024] A course of medical action can be delivering medical
treatment to said subject, for example a medical treatment selected
from a group consisting of the following: a pharmaceutical,
surgery, organ resection, and radiation therapy.
[0025] The course of medical action can also comprise
administration of medical tests, medical imaging of said subject,
setting a specific time for delivering medical treatment, a biopsy,
and/or consultation with a medical professional.
[0026] In yet another aspect, a method of delivering a probability
that a subject has a medical condition to a user is disclosed
comprising: calculating a plurality of posterior probabilities of
the occurrence of a plurality of prostate medical conditions of a
subject having a PSA value and an fPSA value, each at more than one
time thereby having a PSA trend and an fPSA trend, wherein each of
said plurality of posterior probabilities is calculated by
relating: a prior probability of a prostate medical condition; and
a probability of observing said PSA trend and said fPSA trend for
an individual with said prostate medical condition; and a
probability of observing said PSA trend and said fPSA trend for an
individual without said prostate medical condition; and delivering
said plurality of probabilities of said plurality of medical
conditions to a user with an output device. In an embodiment, a
method can further comprise: calculating a second plurality of
posterior probabilities of the occurrence of said plurality of
prostate medical conditions of a subject having a result of a
course of medical action and having a new PSA value and a new fPSA
value, each at more than one time thereby having a second PSA trend
and a second fPSA trend, wherein each of said plurality of
posterior probabilities is calculated by relating: a prior
probability of a prostate medical condition, wherein said prior
probability was calculated using subject information comprising
said result of a course of medical action; and a probability of
observing said second PSA trend and said second fPSA trend for an
individual with said prostate medical condition; and a probability
of observing said second PSA trend and said second fPSA trend for
an individual without said prostate medical condition; and
delivering said second plurality of probabilities of said plurality
of medical conditions to the user with an output device.
INCORPORATION BY REFERENCE
[0027] All publications mentioned in this specification are herein
incorporated by reference to the same extent as if each individual
publication was specifically and individually indicated to be
incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0028] Many features of the invention are set forth with
particularity in the appended claims. A better understanding of the
features and advantages of the invention will be obtained by
reference to the following detailed description that sets forth
illustrative embodiments, in which many of the invention are
utilized, and the accompanying drawings of which:
[0029] FIG. 1 illustrates an exemplary treatment timing flow
chart.
[0030] FIG. 2 shows an exemplary life outcome simulator for a range
of treatment timing scenarios.
[0031] FIG. 3 shows an exemplary Life Score graph of how Life Score
varies for a range of treatment timing.
[0032] FIG. 4 shows an exemplary Life Score curve that is
relatively flat because timing of prostate cancer treatment causes
relatively small changes in well-being and length of life.
[0033] FIG. 5 provides an exemplary overview of an embodiment of a
dynamic screening system.
[0034] FIG. 6 shows some embodiments of modules of the dynamic
screening system.
[0035] FIG. 7 shows an example of the high level inputs and outputs
for estimating the probability of progressing cancer.
[0036] FIG. 8 illustrates an alternative method for creating the
long-term probabilities.
[0037] FIG. 9 demonstrates an embodiment of the personalized
probability distributions and probabilities module uses a four
dimensional frequency generator.
[0038] FIG. 10 shows an embodiment of a four dimensional frequency
generator that calculates personalized probability distributions
and probabilities for the no cancer case in iterative fashion.
[0039] FIG. 11 shows an embodiment of a four dimensional frequency
generator that calculates personalized probability distributions
and probabilities for cancer plus no cancer cases in iterative
fashion.
[0040] FIG. 12 shows an example of the 100 possible buckets of
possible results when each dimension is divided into ten
segments.
[0041] FIG. 13 shows an example of the 10,000 possible buckets of
possible results when each dimension is divided into ten segments
(even though only three of the four dimensions can be
depicted).
[0042] FIG. 14 shows conceptually the bucket of concern defined by
the range of PSA and PSAV results around the observed trend
results.
[0043] FIG. 15 suggests conceptually the hypercube bucket of
concern defined by the range of PSA, PSAV, fPSA % and fPSAV %
results around the observed trend results (even though only three
of the four dimensions can be depicted).
[0044] FIG. 16 shows an exemplary four dimensional frequency
generator for the no cancer case. Each iteration is initiated by
the Monte Carlo iteration controller.
[0045] FIG. 17 shows an exemplary Monte Carlo process for
generating outcomes for year X cancer from a number of probability
distributions, where X is a measure of cancer progression.
[0046] FIG. 18 demonstrates an exemplary pattern of accelerating
PSA caused by progressing prostate cancer.
[0047] FIG. 19 shows a linear trend that best fits the example data
over a ten year period from age 50 to age 60.
[0048] FIG. 20 shows that reducing the estimation window from ten
years to six years reduces the underestimation of PSA at age 60 to
about 1.4 PSA (12.5 actual minus 11.1 for the linear trend).
[0049] FIG. 21 plots an example of estimated PSA at age 60 and a
decline at an accelerating rate as the estimation window size
increases.
[0050] FIG. 22 shows exemplary how the standard deviation of the
estimate of current PSA at age 60 declines as window size
increases.
[0051] FIG. 23 combines the results shown on FIG. 21 and FIG.
22.
[0052] FIG. 24 shows a calculation of the probability of
progressing cancer as a function of window size.
[0053] FIG. 25 illustrates the results of an exemplary linear
function for estimating the PSA trend.
[0054] FIG. 26 and FIG. 27 show how variable trend functions and
window sizes can be added to the no cancer four dimensional
frequency generator and the cancer plus no cancer four dimensional
frequency generator.
[0055] FIG. 28 shows an example of this triangle weighting function
as a function of PSA.
[0056] FIG. 29 shows an example of a weighting function as a
function of PSA and PSA velocity.
[0057] FIG. 30 shows an exemplary Maximum Probability Bayes
system.
[0058] FIG. 31 shows how the results that can be observed for both
PSA only using analytic optimization (the curve maximum for each
curve) using one dimensional response surfaces (the curves).
[0059] FIG. 32 shows how the results can be observed for both PSA
and PSA velocity.
[0060] FIG. 33 shows an exemplary concept of maximum probability
and window sizes in a 3D situation and suggests how it can work in
4D or higher situations.
[0061] FIG. 34 shows the probability of each of these two temporary
benign conditions for anomalous test results over time for a
man.
[0062] FIG. 35 illustrates that prior probabilities may be a
function of age, race, genetics, demographics, past experience with
the conditions and other considerations.
[0063] FIG. 36 shows temporary conditions of the prostate are
partitioned into 7 different condition combinations that are
composed of three different prostate conditions.
[0064] FIG. 37 and FIG. 38 demonstrate a probability generator for
all temporary prostate conditions consolidates output from three
separate probability generators: inflammation prostatitis,
infection prostatitis and other temporary conditions.
[0065] FIG. 39 shows how probability distributions of each prostate
condition can be affected by past medical experience with the
conditions, and the results of imaging, tests, treatment and other
medical procedures.
[0066] FIG. 40 shows, for example, a negative bacterial culture and
no impact from antibiotic treatment may reduce the probability of
infection prostatitis and increase the probability of inflammation
and the probability of other conditions.
[0067] FIG. 41 shows an embodiment of other clinical conditions PSA
increment is the product of the other conditions leak rate
increment, drawn from the other conditions LI % distribution, and
trend PSA from the PSA module.
[0068] FIG. 42 shows an example of how the probability of the
presence of infection (P %) for a man tends to increase with age
and past history of infection.
[0069] FIG. 43 shows the probability density for an infection leak
increment percent (LI %) can depend on past experience.
[0070] FIG. 44 shows the probability density for free PSA % (fPSA
%) can also depend on past experience.
[0071] FIG. 45 shows how the probability of each of these three
benign conditions can change over time for a man.
[0072] FIG. 46 shows an embodiment of how four similar Bayes
processes are used to calculate the probability of the prostate
conditions: volume growth due to BPH, inflammation prostatitis,
infection prostatitis and progressing cancer.
[0073] FIG. 47 shows the status of the prostate is partitioned into
16 different condition combinations that are composed of five
different prostate conditions.
[0074] FIG. 48, FIG. 49, and FIG. 50 show an aspect of the
invention, a probability generator for all prostate conditions
consolidates output from five exemplary separate probability
generators for a healthy prostate and the four prostate that
include without limitation: volume growth due to BPH, inflammation
prostatitis, infection prostatitis and progressing cancer.
[0075] FIG. 51 shows the probability distributions of each prostate
condition can be affected by past experience and the results of
imaging, tests, treatment and other medical procedures as shown
in.
[0076] FIG. 52 shows an embodiment of the no cancer probability
generators.
[0077] FIG. 53 shows an embodiment of a healthy prostate module
that has three distributions for Monte Carlo draws: Vol, PSAD and
fPSA %.
[0078] FIG. 54 shows an embodiment of a BPH volume growth module
that has four distributions for Monte Carlo draws: Vol, VolVel,
PSAD and fPSA %.
[0079] FIG. 55 shows an embodiment of an inflammation prostatitis
module that has three distributions for Monte Carlo draws: L %, LV
% and fPSA %.
[0080] FIG. 56 shows an embodiment of an infection prostatitis
module that has three distributions for Monte Carlo draws: L %, LV
% and fPSA %.
[0081] FIG. 57 shows an embodiment of a first step of a tuning
process of the invention that is to tune the no cancer static
distribution for a given age (t=0), such as age 55.
[0082] FIG. 58 shows exemplary tuning of parameters and validation
of detailed distributions.
[0083] FIG. 59 and FIG. 60 show embodiments of a second step that
is to tune velocity parameters to achieve the no cancer static
distribution for ten years later (t=10), such as age 65.
[0084] FIG. 61 illustrates an exemplary computer system of the
invention comprising a plurality of graphical user interfaces, a
front end server comprising databases, and a back end server
capable of performing calculations of probabilities.
[0085] FIG. 62 illustrates an exemplary method of delivering a
probability that a subject has a medical condition to a user and
using the probability to take a course of medical action.
[0086] FIG. 63 and FIG. 64 illustrate exemplary courses of events
related to a method or system of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0087] Methods, business methods, and systems are provided herein
for integrated healthcare. As the amount of medical information
increases rapidly, including information from multiple biomarkers,
analysis and management of that information becomes more and more
important to extract meaningful conclusions from the information.
Methods and systems, as described herein, provide calculations of
biomarker values into useful analytical data for a user. The
methods and systems have potential in a variety of healthcare
cases, including genomics, diagnosis, point-of-care applications,
pharmaceuticals, and clinic trials. For the purpose of example,
many of the methods and systems are described herein in the context
of analyzing data from men regarding prostate medical
conditions.
[0088] In an aspect, a method utilizes computer-implemented
personalized probability determination systems. In another aspect,
the invention features methods for use in integrated health systems
and methods related to organs of the human body and to cancer.
[0089] A treatment timing system can help men and their medical
advisers choose a time for treatment of prostate cancer. The
Treatment Timing system can build on the results of personalized
probability analysis. The timing of treatment for prostate cancer
can be a balancing act. Early treatment often increases the chance
of cure but may increase the risk of unnecessary treatment and side
effects.
Timing System
[0090] Exemplary methods and systems are introduced briefly herein
along with a flow chart on FIG. 1 as described here. The
Probabilities and Early Warning results from dynamic screening are
an input to the Treatment Timing system. Other relevant information
including personal profile information is entered. Treatment is
selected for analysis by the user or treatments are analyzed in
iterative fashion by the system. The system analyzes a range of
years of early and late warning in iterative fashion. The annual
probability of treatment for each future year is projected based on
the current probability of progressing cancer and years of early
warning from the dynamic screening system in step. The Cancer Cure
Ratio is estimated for treatment each year based on the amount of
early or late warning. The Cure Ratio is used to project the
probability of recurrence after treatment over time and subsequent
progression. The probability of death from prostate cancer is
projected from the risk of subsequent progression for each year of
potential treatment and then cumulated for an overall probability
projection. The risk of death from other causes is considered in
estimating the increase in the overall risk of death for each
future year. For each year of treatment the probability of
treatment in that year is used to weight the subsequent risk of
side effects. The risks for each year of treatment are cumulated to
estimate an overall risk of side effects for each future year.
Changes in Life Score are calculated for the increased risk of
death by year and for the risk of side effects using the Emotional
Weights entered by the user in his Personal profile. The man's
overall Life Score may be reduced by the Life Score Impacts of
increased risks of death and side effects. Results are summarized
for each strategy. A man, medical personnel and other users (for
example, family) can use Life Score simulations to help them choose
the best timing for biopsy and treatment of progressing cancer. For
a biopsy, a doctor uses a device to inject thin hollow needles into
the prostate to extract tissue. Typically, a pathologist examines
the tissue and may provide a diagnosis of prostate cancer. Primary
treatment is intended to cure prostate cancer and can include
surgery to remove the prostate and various types of radiation to
kill the cancer. A pathology report after surgery can provide
useful information about the progress of cancer.
[0091] An exemplary life outcome simulator, as shown on FIG. 2, can
be used to calculate Life Score Impacts and Life Scores on FIG. 1
for a range of treatment timing scenarios. The probability of
progressing cancer from a previous module is an example input. The
user may supply information on his Personal profile. The system may
supply a standard range of treatment timing scenarios.
[0092] In an embodiment, Life Score is a measure of well-being and
length of life, based on the information entered in the profile.
The exemplary Life Score graph on FIG. 3 shows how Life Score
varies for a range of treatment timing. A value of 100% may
represent Life Score in the absence of prostate cancer and serve as
a point of reference. In the example of FIG. 4, The Life Score
curve is relatively flat because timing of prostate cancer
treatment causes relatively small changes in well-being and length
of life. Timing can be measured in years before and after the
Transition Point, (for example, the time of progression when the
cure rate begins to decline steeply) of progressing cancer (year
0). Before the Transition Point the Cure Ratio may decline
relatively slowly. After the Transition Point the Cure Ratio can
drop more steeply as the risk increases that cancer has spread
outside of the prostate.
[0093] The line and treatment diamond on the graph on FIG. 3 may
depend on the primary treatment selected in the profile (for
example, surgery, dual radiation, seed radiation and external
radiation). The treatment diamond on each graph shows the treatment
timing that maximizes Life Score and minimizes Life Score impact.
For Life Scores that are different, one way to interpret the
difference can be in the context of a total life. For example, if
someone expects to live thirty more years, a 3% difference in Life
Score would be equivalent to almost 1 year of life. In the
exemplary figures, the diamond on each graph shows a rough estimate
of biopsy timing that corresponds with the treatment timing that
maximizes Life Score. A first biopsy should occur roughly six
months to a year before the optimal time for treatment, so the
biopsy timing diamond may show up on the graphs approximately six
months to a year or more before the treatment timing diamond. The
actual size of the biopsy lead time depends on a variety of
factors.
[0094] In an embodiment, Life Score Impact is the reduction in Life
Score by side effects and death from prostate cancer. Life Score
Impact can measure the drop from 100% on the Life Score graph of
the previous FIG. 3. The graph on FIG. 4 shows an exemplary Life
Score Impact for the range of treatment timing. The bottom curve
shows the total Life Score Impact for the treatment that is chosen.
It is the sum of reduction in Life Score from side effects and
death from prostate cancer. The curve is more pronounced than on
the previous graph because the scale has been expanded. It does not
span the full range of possible impacts from 0% to 100%. The
treatment diamond on each graph shows the treatment timing that
maximizes Life Score and minimizes Life Score impact. The diamond
on each graph shows a rough estimate of biopsy timing that
corresponds with the treatment timing that maximizes Life Score.
The top curve shows the Life Score Impact of all side effects. The
impact is greatest on the left when the risk of unnecessary
treatment is greatest. The middle curve shows the Life Score Impact
of death from prostate cancer. The impact is greatest on the right
when late treatment leads to a decrease in cure rate and an
increased risk of cancer death.
[0095] Disclosed herein are computer-implemented personalized
probabilities determination systems and methods for use in
integrated health systems and methods related to organs of the
human body and to cancer. For example, a system and method is
disclosed herein for estimating trends in biomarkers and
calculating the probability of certain conditions of one or more
organs of the human body. This exemplary system and method could be
used for any condition of any organ of the human body. An
application to the male prostate with a focus on progressing
prostate cancer is disclosed as an example here without
limitation.
Personalized Probabilities
[0096] In an aspect, a method is disclosed of performing a course
of medical action for a medical condition of a subject comprising:
obtaining a first value of at least one biomarker from a
subject;
[0097] sending said first value to a computer system that
calculates a first plurality of posterior probabilities of the
occurrence of a plurality of medical conditions of said subject
using said first value, wherein said plurality of medical
conditions comprises at least a first and second medical condition;
receiving said first plurality of posterior probabilities;
performing a first course of medical action for the first medical
condition based on said first plurality of posterior probabilities;
observing a result of said first course of medical action;
obtaining a second value of at least one biomarker from said
subject; sending said second value and said result of said first
course of the medical action to said computer system that
calculates a second plurality of posterior probabilities of the
occurrence of said plurality of medical conditions of said subject,
wherein said calculation uses said at second value and said result;
receiving said second plurality of posterior probabilities; and
performing a second course of medical action for the second medical
condition based on said second plurality of posterior
probabilities.
[0098] In an embodiment, the first or second value is a PSA value
or fPSA value. In another embodiment, the subject is a human, for
example a patient.
[0099] In an embodiment, a computer system comprises a device for
network communication, a storage unit, and a processor. The
computer system can comprise a Monte Carlo engine.
[0100] In an embodiment, sending comprises entering said first and
second values into a webpage or using a device that transmits
either or both of said first and second values to said computer
system through a wireless network.
[0101] In an embodiment, first and second values are a first and
second biomarker trend of biomarker values over a period of time. A
computer system can calculate each of said first plurality of
posterior probabilities by relating: a prior probability of a
medical condition; a probability of observing said first biomarker
trend for an individual with said medical condition; and a
probability of observing said first biomarker trend for an
individual without said medical condition. A computer system can
calculate each of said second plurality of posterior probabilities
by relating: a prior probability of a medical condition, wherein
said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability
of observing said second biomarker trend for an individual with
said medical condition; and a probability of observing said second
biomarker trend for an individual without said medical
condition.
[0102] In an embodiment, a plurality of medical conditions are
prostate medical conditions, for example they can be selected from
the group consisting of the following: prostatitis due to
inflammation, prostatitis due to infection, prostate cancer, benign
prostate hyperplasia, and no prostate disease.
[0103] In an embodiment, receiving comprises viewing a display of
said posterior probabilities, for example a display on an output
device. An output device can be selected from the group consisting
of the following: a computer, a webpage, an electronic medical
record, a printout, and a personal electronic device.
[0104] In an embodiment, a first or second course of medical action
is delivering medical treatment to said subject, such as a medical
treatment is selected from a group consisting of the following: a
pharmaceutical, surgery, organ resection, and radiation therapy. In
an embodiment, a pharmaceutical comprises a chemotherapeutic
compound for cancer therapy. In another embodiment, the first or
second course of medical action comprises administration of medical
tests or medical imaging of said subject or setting a specific time
for delivering medical treatment or a biopsy or a consultation with
a medical professional.
[0105] In an aspect of the invention, a method of delivering a
probability that a subject has a medical condition to a user
comprises: calculating a plurality of posterior probabilities of
the occurrence of a plurality of medical conditions of a subject
having a biomarker trend, wherein said biomarker trend comprises
biomarker values from said subject at more than one time, and
wherein each of said plurality of posterior probabilities is
calculated by relating: a prior probability of the occurrence of
each of said plurality of medical conditions; and a probability of
observing said biomarker trend for an individual with each medical
condition; and a probability of observing said biomarker trend for
an individual without each medical condition; and delivering said
plurality of probabilities of said plurality of medical conditions
to a user with an output device.
[0106] In another aspect, a method of delivering a probability that
a subject has a medical condition to a user comprises: calculating
a plurality of posterior probabilities of the occurrence of a
plurality of medical conditions of a subject having a result of a
course of medical action and having a biomarker trend, wherein said
biomarker trend comprises biomarker values from said subject at
more than one time, and wherein each of said plurality of posterior
probabilities is calculated by relating: a prior probability of the
occurrence of each of said plurality of medical conditions, wherein
said prior probability was calculated using subject information
comprising said result of a course of medical action; a probability
of said biomarker trend for an individual with each medical
condition; and a probability of said biomarker trend for an
individual without each medical condition; and delivering said
plurality of probabilities of said plurality of medical conditions
to a user with an output device. A biomarker trend can be a PSA
trend or fPSA trend.
[0107] In an embodiment, an output device is selected from the
group consisting of the following: a computer, a webpage, an
electronic medical record, a printout, and a personal electronic
device.
[0108] A course of medical action can be delivering medical
treatment to said subject, for example a medical treatment selected
from a group consisting of the following: a pharmaceutical,
surgery, organ resection, and radiation therapy.
[0109] The course of medical action can also comprise
administration of medical tests, medical imaging of said subject,
setting a specific time for delivering medical treatment, a biopsy,
and/or consultation with a medical professional.
[0110] In yet another aspect, a method of delivering a probability
that a subject has a medical condition to a user is disclosed
comprising: calculating a plurality of posterior probabilities of
the occurrence of a plurality of prostate medical conditions of a
subject having a PSA value and an fPSA value, each at more than one
time thereby having a PSA trend and an fPSA trend, wherein each of
said plurality of posterior probabilities is calculated by
relating: a prior probability of a prostate medical condition; and
a probability of observing said PSA trend and said fPSA trend for
an individual with said prostate medical condition; and a
probability of observing said PSA trend and said fPSA trend for an
individual without said prostate medical condition; and delivering
said plurality of probabilities of said plurality of medical
conditions to a user with an output device. In an embodiment, a
method can further comprise: calculating a second plurality of
posterior probabilities of the occurrence of said plurality of
prostate medical conditions of a subject having a result of a
course of medical action and having a new PSA value and a new fPSA
value, each at more than one time thereby having a second PSA trend
and a second fPSA trend, wherein each of said plurality of
posterior probabilities is calculated by relating: a prior
probability of a prostate medical condition, wherein said prior
probability was calculated using subject information comprising
said result of a course of medical action; and a probability of
observing said second PSA trend and said second fPSA trend for an
individual with said prostate medical condition; and a probability
of observing said second PSA trend and said second fPSA trend for
an individual without said prostate medical condition; and
delivering said second plurality of probabilities of said plurality
of medical conditions to the user with an output device.
[0111] A system to perform the Bayes calculation of the probability
of progressing cancer can be configured with the following
components: 1) prior probabilities of cancer at various stages of
progression; 2) probability of the observation of various biomarker
trends conditional on no progressing cancer; and 3) probability of
the observation of various biomarker trends conditional on cancer
at various stages of progression.
[0112] A system can be configured for generating one or both of the
last two categories of probabilities for an individual man with
specific observed biomarker trends and corresponding measurement
uncertainty in those trends.
[0113] For example, consider a man concerned about prostate cancer
with a series of PSA and free PSA biomarker results from blood
tests. Trends can be estimated for each biomarker and analyzed
using methods previously disclosed. For example, trend PSA velocity
is the annual rate of change in trend PSA; trend free PSA % is
trend free PSA divided by trend PSA; and trend free PSA velocity %
is trend free PSA velocity divided by trend PSA velocity. The
results can be as in Table 1.
TABLE-US-00001 TABLE 1 Example of values for biomarker trends.
trend Value Standard Deviation trend PSA 3.0 0.4 trend PSA velocity
0.40 0.20 trend free PSA % 17.0% 2.0% trend free PSA velocity %
6.0% 3.0%
[0114] Other information about the man may be available, including,
age, measurement of prostate volume in some cases, and other
factors that may affect the conditional probabilities.
[0115] Typically, no highly specific conditional distributions can
be estimated directly from available population data. In an aspect,
a disclosed method calculates the needed personalized
probabilities.
[0116] In an embodiment, a method comprises creating personalized
biologic probability models of several states: 1) no cancer
conditions of the prostate: healthy and volume growth; 2) cancer at
various stages of progression, and 3) combined models of no cancer
conditions and various stages of cancer progression. Those models
are then combined with trend uncertainty models to create an
overall multi-dimensional distribution or part of the distribution
relevant to the specific trend results. The distributions can be
multi-dimensional in that trend values and trend velocities, or
annual rates of change, are considered for at least one biomarker,
such as PSA. The disclosed example describes a method for creating
four dimensional distributions and probabilities for two
biomarkers: PSA and free PSA. In an embodiment, higher dimensional
distributions and probabilities are needed when additional
biomarkers are considered.
[0117] For example, Monte Carlo methods may be used to create four
dimensional probability distributions for PSA, PSAV, fPSA % and
fPSAV % from random draws from the probability distributions of the
underlying biologic and trend uncertainty models. A calculation
process can be time consuming and slow a response user inputting
and receiving information on the internet or world wide web. The
complexity and time of calculation can increase exponentially as
additional biomarkers become available and are incorporated into
the method. Therefore, efficient methods of calculating the
probabilities can be beneficial.
[0118] For example, a method focuses on the probabilities of the
observed trend values rather than larger four dimensional
probability distributions for PSA, PSAV, fPSA % and fPSAV % for the
full range of possible outcomes. This approach reduces the amount
of calculations necessary to calculate the personalized
probabilities needed for the Bayes calculations. In an embodiment,
the reduction is achieved in practice using a hierarchical triage
approach that aborts a Monte Carlo iteration as soon as one of the
values falls outside the target range for first PSA, then PSAV,
then fPSA % and finally fPSAV %.
[0119] A prostate dynamic screening system can help men and their
doctors screen for progressing cancer, long-term conditions and
short-term conditions. It provides early warning of progressing
cancer while reducing the probability of unnecessary treatment and
side effects. The results can be useful inputs to the optimal
Treatment Timing system. The prostate is the organ of the body used
in many of the examples described herein, however, the methods and
systems described herein are useful for a variety of biomarkers for
a variety of diseases. Conditions used as examples are progressing
prostate cancer, prostate volume growth caused by Benign Prostatic
Hyperplasia (BPH) and infections of the prostate. Both PSA and free
PSA tests can be used for screening. Other tests may supplement
them or replace them.
[0120] The flow chart on FIG. 5 provides an exemplary overview of
an embodiment of a dynamic screening system. For one person,
biomarker and image results are input on the left. For the
prostate, these are PSA and free PSA test results and ultrasound
measurements of prostate volume. The experience of other men is
input from the top. A diagnosis of temporary conditions comes out
the bottom. For the prostate, an infection is the most common and
serious temporary condition. Diagnoses of progressing cancer and
long-term conditions (volume growth due to BPH for the prostate)
are output on the right. All output becomes part of all screening
history and is fed back as the experience of other men to increase
the power of dynamic screening.
[0121] The flow chart on FIG. 6 shows some embodiments of modules
of the dynamic screening system. A user can complete a profile. The
prostate strategy system can analyze strategy alternatives and can
choose the best life strategy.
[0122] Using the dynamic screening system, the man can follow
suggestions about the type and timing of primary and secondary
screening tests. Typically the system can recommend a baseline
prostate volume study and annual PSA and free PSA tests. Free PSA
tests are currently recommended; however, other tests may be
recommended in the future in conjunction with free PSA or to
substitute for it. Tests results can be entered into the system for
analysis and guidance. Steadily increasing PSA due to prostate
enlargement from BPH, if rapid enough, may lead the system to
suggest periodic prostate volume measurements to define the rate of
growth. Tests results can be entered into the system for analysis
and guidance.
[0123] The dynamic screening system can recognize the false alarms
caused by infection and other temporary conditions, provide a
calming perspective, suggest new PSA and free PSA tests after the
infection or condition has passed, and analyze the results of new
tests.
[0124] The dynamic screening system can recognize early warning of
possible cancer progression and suggest additional confirmation
tests. Confirmation tests may include other components of PSA such
as Pro PSA and any other useful new markers developed in the
future. In addition, a new prostate volume study may be suggested,
perhaps using more expensive technology if rapid prostate
enlargement is a factor. A second round of confirmation tests can
be suggested--perhaps six months after the first. Additional
confirmation tests can be suggested until progression has been
confirmed or rejected.
[0125] The dynamic screening system can confirm a high probability
of progressing cancer when its calculation shows the probability is
high enough to warrant consideration of biopsy and treatment
[0126] The optimal treatment timing system can calculate the
optimal schedule for biopsy and treatment based on ongoing
screening tests and the information entered in the profile. The man
and his advisors can use the results to schedule a first biopsy and
subsequent treatment.
[0127] In the feedback learning process, the man or his doctor will
provide follow up information for the system to analyze and
incorporate for use by other men.
[0128] The exemplary long-term probabilities module of FIG. 6
estimates the probabilities of one or more long-term conditions,
such as progressing cancer or prostate volume growth. FIG. 7 shows
an example of the high level inputs and outputs for estimating the
probability of progressing cancer. Prior probabilities are the
starting point in FIG. 6. Trend residual velocities come from FIG.
6. Velocities and trends may be used in other embodiments. The
long-term probabilities module on FIG. 7 adjusts the prior
probabilities of progressing cancer based on how the trend residual
velocities compare with patterns for progressing cancer and the
predicted values for no cancer. A variety of methods can be used to
estimate the probability, including Bayesian and simulation
methods. The process can involve a variety of cancer stages,
characterized by years of early warning, which is measured as years
before the transition point, defined as the time of progression
when the cure rate begins to decline steeply. Therefore, a module
may consider a range of progressing cancer possibilities (different
years of early warning) and a no-cancer (not present or not
progressing) possibility defined by the no-cancer predicted values.
For each of these possibilities a probability distribution can be
constructed that can be characterized by a mean and by variation,
which can be characterized by standard deviations. There are two
sources of variation that can be considered. First, trend variation
can be caused by possibly random variation in test results. Second,
biologic variation can be caused by differences among men or for a
specific man over time.
[0129] The approaches described herein can be used as an
alternative method for creating the long-term probabilities, as
shown on FIG. 8. The long-term probabilities module is split into a
personalized probability distributions module and probabilities
module and a Bayes long-term probabilities module. The Bayes
calculations in the second module have been disclosed in the above
incorporated references. The first module is described below. The
outputs of module are probabilities of the observed trend results:
PSA, PSAV, fPSA % and fPSAV % conditional on no cancer and cancer
for various years (X). These are created using personal information
and input from biologic and trend models, as disclosed below.
[0130] In an embodiment, the personalized probability distributions
and probabilities module uses a four dimensional frequency
generator, shown on FIG. 9, which calculates personalized
probability distributions and probabilities for cancer and no
cancer cases in iterative fashion. Each iteration is initiated by
the Monte Carlo iteration controller and ended by the Monte Carlo
iteration completion module, which returns control to the
controller. For each iteration, trend values for a healthy prostate
are generated from probability distributions. Trend values for
prostate volume growth are generated from probability
distributions. No cancer values are calculated in module as the sum
of values. The values for each iteration are added to the
appropriate four dimensional bucket defined by ranges in four
dimensions. As the number of iterations increase, frequency
distributions for the no cancer case are built up and output at the
end of the process. For each iteration, trend values for each year
X cancer case are generated from probability distributions. A range
of cases are calculated for year X cancers, where X is a measure of
cancer progression. Values for each year X cancer plus no cancer
case are calculated in module as the sum of values. The values for
each iteration are added to the appropriate four dimensional bucket
defined by ranges in four dimensions. As the number of iterations
increase, frequency distributions for each year X cancer plus no
cancer case are built up and output at the end of the process.
[0131] It can be computationally more efficient to use independent
Monte Carlo processes for the no cancer case and cancer plus no
cancer cases. In an embodiment, the four dimensional frequency
generator, shown on FIG. 10, calculates personalized probability
distributions and probabilities for the no cancer case in iterative
fashion. Each iteration is initiated by the Monte Carlo iteration
controller and ended by the Monte Carlo iteration completion
module, which returns control to the controller. For each
iteration, trend values for a healthy prostate are generated from
probability distributions. Trend values for prostate volume growth
are generated from probability distributions. No cancer values are
calculated as the sum of values. The values for each iteration are
added to the appropriate four dimensional bucket defined by ranges
in four dimensions. As the number of iterations increase, frequency
distributions for the no cancer case are built up.
[0132] In another embodiment, the four dimensional frequency
generator, shown on FIG. 11, calculates personalized probability
distributions and probabilities for cancer plus no cancer cases in
iterative fashion. Each iteration is initiated by the Monte Carlo
iteration controller and ended by the Monte Carlo iteration
completion module, which returns control to the controller. For
each iteration, trend values for a healthy prostate are generated
from probability distributions. Trend values for prostate volume
growth are generated from probability distributions. No cancer
values are calculated as the sum of values. For each iteration,
trend values for each year X cancer case are generated from
probability distributions. A range of cases are calculated for year
X cancers, where X is a measure of cancer progression. Values for
each year X cancer plus no cancer case are calculated in module as
the sum of values from. The values for each iteration are added to
the appropriate four dimensional bucket defined by ranges in four
dimensions. As the number of iterations increase, frequency
distributions for each year X cancer plus no cancer case are built
up.
[0133] The approach described in this example generates extensive
four dimensional distributions that can be used to find the
probabilities needed for the Bayes calculations of the probability
of progressing cancer. However, the calculations can be time
consuming and cause delays in real-time responses to users. The
approach of focused probabilities is discussed below to address
this if it is an issue for a situation at hand. The number of
calculations and the time to perform them can be reduced
substantially by focusing narrowly on the probabilities needed for
the Bayes calculations rather than on generating extensive four
dimensional distributions. Detailed methods for focusing on the
needed probabilities are disclosed below.
[0134] For an exemplary biomarker, such as PSA, two dimensions may
be needed, for example, PSA and PSA velocity (PSAV). A two
dimensional rectangle of possible Monte Carlo results can be
created by dividing each dimension into segments. The example in
FIG. 12 shows the 100 possible buckets of possible results when
each dimension is divided into ten segments.
[0135] As an example, the segments for each of the two dimensions
can be as described in Table 2 and 3.
TABLE-US-00002 TABLE 2 Ten segments for the PSA dimension >=0
and <1 >=1 and <2 >=2 and <3 >=3 and <4 >=4
and <5 >=5 and <6 >=6 and <7 >=7 and <8 >=8
and <9 >=9
TABLE-US-00003 TABLE 3 Ten segments for the PSAV dimension >=0.0
and <0.1 >=0.1 and <0.2 >=0.2 and <0.3 >=0.3 and
<0.4 >=0.4 and <0.5 >=0.5 and <0.6 >=0.6 and
<0.7 >=0.7 and <0.8 >=0.8 and <0.9 >=0.9
[0136] In another example, for two tests, such as PSA and free PSA,
four dimensions can be important, for example PSA, PSAV, fPSA % and
fPSAV %. A four dimensional hyper cube of possible Monte Carlo
results can be created by dividing each dimension into segments.
The example of FIG. 13 suggests the 10,000 possible buckets of
possible results when each dimension is divided into ten segments
(even though only three of the four dimensions can be
depicted).
[0137] In another example, consider a man concerned about prostate
cancer with a series of PSA biomarker results from blood tests.
Trends can be estimated for each biomarker and analyzed using
methods previously disclosed. For example, the results can be as
described in Table 4.
TABLE-US-00004 TABLE 4 Trend Value Standard Deviation trend PSA 3.0
0.4 trend PSA velocity 0.40 0.20
The bucket used to collect the frequency of this outcome can be as
described in Table 5.
TABLE-US-00005 TABLE 5 PSA = 3.0 +/- 0.5 or PSA >2.5 and <3.5
PSAV = 0.4 +/- 0.05 or PSAV >0.35 and <0.45
The gray rectangle on the table of FIG. 14 shows conceptually the
bucket of concern defined by the range of PSA and PSAV results
around the observed trend results. For one case, other buckets that
are not shaded are not of interest.
[0138] For a man concerned about prostate cancer with a series of
PSA and free PSA biomarker results from blood tests, trends can be
estimated for each biomarker and analyzed using methods previously
disclosed. The results can be as in Table 6.
TABLE-US-00006 TABLE 6 trend Value Standard Deviation trend PSA 3.0
0.4 trend PSA velocity 0.40 0.20 trend free PSA % 17.0% 2.0% trend
free PSA velocity % 6.0% 3.0%
[0139] The bucket used to collect the frequency of this outcome can
be as in Table 7.
TABLE-US-00007 TABLE 7 PSA = 3.0 +/- 0.5 or PSA >2.5 and <3.5
PSAV = 0.4 +/- 0.05 or PSAV >0.35 and <0.45 fPSA % = 17.0%
+/- 2.0% or fPSA % >15.0% and <19.0% fPSAV % = 6.0% +/- 2.0%
or fPSAV % >4.0% and <8.0%
[0140] The small cube inside the large cube shown by the example in
FIG. 15 suggests conceptually the hypercube bucket of concern
defined by the range of PSA, PSAV, fPSA % and fPSAV % results
around the observed trend results (even though only three of the
four dimensions can be depicted). For one case, the other buckets
that are outside the small cube are not of interest. In general,
for a single case trend values for PSA, PSAV, fPSA % and fPSAV %
are known, which is a point in the 4D hyper cube. A small hyper
cube bucket around the point can be created to collect Monte Carlo
results that fall within the ranges. The frequency of the results
in the bucket can be used to estimate the probability of the
results.
TABLE-US-00008 TABLE 8 trend PSA +/- PSA Range Delta trend PSAV +/-
PSAV Range Delta trend fPSA % +/- fPSA % Range Delta trend fPSAV %
+/- fPSAV % Range Delta
Monte Carlo results that fall with the bucket, like the solid dot
in the small cube of FIG. 15, are recorded; and results that fall
outside the bucket, like the circle in the large cube of FIG. 15,
are not recorded.
[0141] FIG. 16 shows an exemplary four dimensional frequency
generator for the no cancer case. Each iteration is initiated by
the Monte Carlo iteration controller. For each iteration, PSA is
calculated in module using Monte Carlo methods. The process stops
for this iteration if PSA falls outside of the target range of the
bucket, but the process continues if PSA falls within the target
range of the bucket. If the iteration continues, PSAV is calculated
in module using Monte Carlo methods. The process stops for this
iteration if PSAV falls outside of the target range of the bucket,
but the process continues if PSAV falls within the target range of
the bucket. If the iteration continues, fPSA % is calculated in
module using Monte Carlo methods. The process stops for this
iteration if fPSA % falls outside of the target range of the
bucket, but the process continues if fPSA % falls within the target
range of the bucket. If the iteration continues, fPSAV % is
calculated in module using Monte Carlo methods. The process stops
for this iteration if fPSAV % falls outside of the target range of
the bucket, but the process continues if fPSAV % falls within the
target range of the bucket. The four dimensional frequency
collector keeps track of the number of Monte Carlo iterations
started and the number of outcomes that fall in the 4D bucket.
Frequency is calculated by dividing the number of outcomes in the
bucket by the number of iterations started. Finally, control is
passed to the Monte Carlo iteration completion module.
[0142] FIG. 17 demonstrates an embodiment of a four dimensional
frequency generator for each year X cancer plus no cancer case.
Each iteration is initiated by the Monte Carlo iteration
controller. For each iteration, PSA is calculated using Monte Carlo
methods. The process stops for this iteration if PSA falls outside
of the target range of the bucket, but the process continues if PSA
falls within the target range of the bucket. If the iteration
continues, PSAV is calculated using Monte Carlo methods. The
process stops for this iteration if PSAV falls outside of the
target range of the bucket, but the process continues if PSAV falls
within the target range of the bucket. If the iteration continues,
fPSA % is calculated using Monte Carlo methods. The process stops
for this iteration if fPSA % falls outside of the target range of
the bucket, but the process continues if fPSA % falls within the
target range of the bucket. If the iteration continues, fPSAV % is
calculated using Monte Carlo methods. The process stops for this
iteration if fPSAV % falls outside of the target range of the
bucket, but the process continues if fPSAV % falls within the
target range of the bucket. The four dimensional frequency
collector keeps track of the number of Monte Carlo iterations
started and the number of outcomes that fall in the 4D bucket.
Frequency is calculated by dividing the number of outcomes in the
bucket by the number of iterations started. Finally, control is
returned to the Monte Carlo iteration completion module.
[0143] FIG. 17 shows an exemplary Monte Carlo process for
generating outcomes for year X cancer from a number of probability
distributions, where X is a measure of cancer progression. For
example, X can be measured as the number of years before or after
the Transition Point, defined as the time of progression when the
cure rate begins to decline steeply. Other reference points for
measuring X may work as well. In this example, fifteen year X cases
can be considered as in Table 9.
TABLE-US-00009 TABLE 9 2 Years After the Transition Point 1 Year
After the Transition Point 0 Years = At the Transition Point 1 Year
Before the Transition Point 2 Years Before the Transition Point 3
Years Before the Transition Point 4 Years Before the Transition
Point 5 Years Before the Transition Point 6 Years Before the
Transition Point 7 Years Before the Transition Point 8 Years Before
the Transition Point 9 Years Before the Transition Point 10 Years
Before the Transition Point 11 Years Before the Transition Point 12
Years Before the Transition Point
Choices can increase about the functional form of the trend and the
window of time over which the trend is estimated as more test
results become available over longer periods of time. Better
choices obtain more and more valuable information from any given
number of test results. An example is presented here of a one
dimensional case where only a linear functional form is considered
and the impact of a range of window sizes is studied.
[0144] In an aspect, four-dimensional frequency distributions from
the Monte Carlo generator as described herein may be pre-computed.
For the test-result types (each of which corresponds to one of the
dimensions of the frequency distribution) that are available, the
trend variation for the dimension (as described herein can be
compared directly against the generated frequency distribution by
the pre-computations. This evaluation produces the probabilities of
observing the trend evidence under the assumption of the presence
or absence of conditions such as prostate cancer. The frequency
distributions and the trend-variation distributions can be smoothed
by any number of strategies and thus captured by a single equation
or a set of several equations, or they can be captured as frequency
values in discrete buckets. The evaluation of one distribution
weighted by the other may therefore involve either continuous or
discrete variables. The multi-dimensional frequency distribution
lends itself to being pre-computed and stored because it is based
largely on static values describing the overall population and is
personalized for an individual subject by a small number of inputs
which capture some fundamental characteristics of the subject. For
each discrete combination of those inputs a frequency distribution
can be stored. For a subject whose values fall between sets of
biomarker values which were used to create stored distributions,
interpolation techniques such as linear interpolation or design of
experiments may be used to extract a personalized distribution.
[0145] FIG. 18 demonstrates an exemplary pattern of accelerating
PSA caused by progressing prostate cancer. At age 50 on the left
healthy PSA starts at 1.0 and remains constant for over a year. PSA
starts to accelerate at an increasing rate until it reaches about
12.5 at age 60 on the right. The dotted line on FIG. 19 shows a
linear trend that best fits the example data over a ten year period
from age 50 to age 60. The line does not fit the curved data
perfectly. The line underestimates PSA from age 50 to about age 52.
It overestimates PSA from about age 52 to just over age 58. It
underestimates PSA from just over age 58 until age 60. At age 60
when the linear trend underestimates PSA by about 3.2 PSA (12.5
actual minus 9.3 for the linear trend).
[0146] The estimate of current PSA at age 60 can be improved by
shortening the window over which the linear trend is estimated. The
dashed line on FIG. 20 shows that reducing the estimation window
from ten years to six years reduces the underestimation of PSA at
age 60 to about 1.4 PSA (12.5 actual minus 11.1 for the linear
trend). The solid line on FIG. 20 shows that reducing the
estimation window from six years to two years further reduces the
underestimation of PSA at age 60 to less than 0.1 PSA (12.5 actual
minus more than 12.4 for the linear trend).
[0147] FIG. 21 plots an example of estimated PSA at age 60 and a
decline at an accelerating rate as the estimation window size
increases.
[0148] Increasing the window size can increase the number of tests
considered and the length of time over which they are considered.
More tests over a longer time can stabilize the trend and reduce
the standard deviation in the estimate of current PSA at age 60
caused by random variation in the PSA test results. The example of
FIG. 22 shows how the standard deviation of the estimate of current
PSA at age 60 declines as window size increases.
[0149] FIG. 23 combines the results shown on FIG. 21 and FIG. 22.
The standard deviation of current PSA is plotted against the
corresponding estimate of current PSA. The results for a ten year
window are shown at the bottom left of the curve, and the results
for a two year window are shown at the top right of the curve. The
steep slope near the top right of the curve suggests that
increasingly short windows provide very little benefit in terms of
an increase in estimated PSA but lead to increasing costs in terms
of steeply increasing standard deviations.
[0150] In an embodiment, a Bayesian probability of progressing
cancer can depend on both current estimates of the trends and on
the confidence in them. A higher PSA leads to a higher probability
if all other variables remain unchanged. In contrast, a higher
standard deviation leads to a lower probability if all other
variables remain unchanged because there is less confidence in the
estimate of current PSA. Changing window size may either increase
or decrease the probability of progressing cancer. For example, a
reduction in widow size will increase the estimate of PSA, which
will increase the probability, but a reduction in window size will
increase the standard deviation, which will decrease the
probability. The outcome for probability depends on which of these
two effects is stronger. FIG. 24 shows a calculation of the
probability of progressing cancer as a function of window size. At
the right, the window size is a large ten years, and the
probability is low because the correspondingly low PSA estimate
dominates. The probability increases as the window size decreases
from ten years to about five years, where the maximum probability
is reached. Further reductions in window size from five years to
two years cause the probability to decrease gradually as the cost
of increasing standard deviation outweighs the benefit of
increasing PSA estimates.
[0151] In these examples, a linear function for estimating the PSA
trend has been considered. These results are shown as the solid
curve on FIG. 25. FIG. 20 shows how the linear function does an
increasingly poor job of matching a curved trend as the window size
increases. Dynamic screening can use higher order functions to
match curved trends more closely. An exponential function is the
preferred higher order function because on average progressing
cancer accelerates in an exponential fashion, but other higher
order functions can be used. Higher order functions, like an
exponential function, provide better fits of curved trends at the
expense of higher standard deviations. Standard deviations are
higher because the increased degrees of freedom make the trend
estimates more sensitive to uncertainty in the test results. The
dashed curve on FIG. 25 shows for an exponential function a
calculation of the probability of progressing cancer as a function
of window size. At the left, the window size is a small two years,
and the probability is relatively low because the cost of a large
standard deviation outweighs the benefit of a large PSA estimate.
The probability increases as the window size increases from two
years to about seven years, where the maximum probability is
reached. Increased window size increases probability because of the
benefit of decreasing standard deviation at little cost from
minimally decreasing PSA estimate. Further increases in window size
from seven years to ten years cause the probability to decrease
gradually as the cost of increasing standard deviation outweighs
the benefit of increasing PSA estimates.
[0152] Test frequency and the length of the test period help
determine which trend function produces the maximum probability of
progressing cancer. On FIG. 25 for when seven to ten years of test
results are available the maximum probability is reached using the
exponential function (dashed line) with a window size of seven
years. With five to seven years of test results the maximum
probability is reached using the exponential function (dashed
curve) and the maximum window size available (equal to the length
of the test period). With less than five years of tests results the
maximum probability is reached using the linear function (solid
curve) and the maximum window size available (equal to the length
of the test period).
[0153] FIG. 26 and FIG. 27 show how variable trend functions and
window sizes can be added to the no cancer four dimensional
frequency generator and the cancer plus no cancer four dimensional
frequency generator. The trend Function and Window Size Controller
determines the combination of functions and window sizes used for
each run of the Monte Carlo No Cancer Four Dimensional Frequency
Generator and No Cancer Four Dimensional Frequency Generator, turns
control over the Frequency Generator to run a series of Monte Carlo
iterations and finally keeps track of the results returned. The
trend function is used to estimate current PSA and PSAV and the
often different window sizes for PSA and PSAV. The trend function
is used to estimate current PSA, PSAV, fPSA and fPSAV needed to
calculate an estimate of current fPSA % and fPSAV % and the often
different window sizes for fPSA % and fPSAV %. The trend Function
and Window Size Controller continues to vary combinations of
functions and window sizes until enough have been run to determine
the maximum probability with reasonable accuracy.
[0154] It can take a large amount of time to run the dynamic
screening system for a sufficiently wide range of combinations of
functions and window sizes. Some time can be saved by reducing the
number of iterations for each Monte Carlo run. However, reducing
iterations increases the risk that only a small number of hits will
be detected in the bucket. A small number of hits can make the
probability of progressing overly sensitive to random hits. This
sensitivity can be reduced by the 4D Frequency Weighting in Module.
Hits detected in the bucket are weighted by a function of the 4D
distance from the observed values at the center of the bucket. Hits
at the center are weighted most highly, and hits farther away are
less heavily weighted. The weighting function reduces the impact of
near misses and marginal hits and reduces the sensitivity of
progressing cancer to them. The weighting function can take the
following form:
Wfn(.DELTA.)=the greater of O and 1-c*.DELTA., where cis a
constant
.DELTA.=(n .DELTA.PSA 2+n .DELTA.PSAV 2+n .DELTA.fPSA % 2+n
.DELTA.fPSAV % 2) 0.5
n .DELTA.PSA=(PSA-tPSA)/nPSA [0155] where tPSA is the current value
of the trend PSA [0156] where nPSA is a normalizing PSA
(possibly=tPSA)
[0156] n .DELTA.fPSA %=(fPSA %-tPSA %)/nPSA % [0157] where tPSA %
is the current value of the trend nfPSA % [0158] where nfPSA % is a
normalizing fPSA % (possibly=tfPSA %)
[0158] n .DELTA.fPSAV %=(fPSAV %-tfPSA %)/nfPSAV % [0159] where
tfPSAV % is the current value of the trend fPSAV % [0160] where
tfPSAV % is a normalizing fPSAV % (possibly=tfPSAV %)
[0160] n .DELTA.fPSAV %=(fPSAV %-tfPSAV %)/nfPSAV % [0161] where
tfPSAV % is the current value of the trend fPSAV % [0162] where
nfPSAV % is a normalizing fPSAV % (possibly=tfPSAV %)
[0163] FIG. 28 shows an example of this triangle weighting function
as a function of PSA. The weight is 0 for PSA from 0 to 3. The
weight increases linearly from 0 at PSA 3 to 1 at PSA 6. The weight
decreases linearly from 1 at PSA 6 to 0 at PSA 9. The weight is 0
for PSA greater than 9.
[0164] FIG. 29 shows an example of a weighting function as a
function of PSA and PSA velocity. The weight is 1 at PSA of 6 and
PSAV of 0.6, shown by the star. The weight drops to zero at the
outer oval centered on the star. Corresponding weighting functions
can be constructed for thee dimensions, four dimensions or more
dimensions.
[0165] An exemplary Maximum Probability Bayes system is shown on
FIG. 30. The trend Function and Window Size Controller generates
four dimensional frequency distributions for no cancer and cancer
plus no cancer cases for an appropriate variety of trend functions
and window sizes, as shown by FIG. 26 and FIG. 27. The
corresponding conditional probabilities are fed to the Bayse
Long-Term Probabilities Maximization module. The maximum
probability can be estimated and the corresponding functions and
window sizes using one or more of a variety of techniques that can
include design of experiments, response surface methods, analytic
optimization, hill climbing optimization and any other methods that
may be effective.
[0166] For example, FIG. 31 shows how the results that can be
observed for both PSA only using analytic optimization (the curve
maximum for each curve) using one dimensional response surfaces
(the curves). Maximum probability is reached using the linear
function (solid curve) and maximum window size for test periods
from two to five years. It is reached using the exponential
function (dashed curve) for test periods greater than five years
with maximum window size for test periods from five to seven years
and a seven year window size for longer test periods.
[0167] FIG. 32 shows how the results that can be observed for both
PSA and PSA velocity. The window size for PSA is shown on the
horizontal axis. The window size for PSAV is shown on the vertical
axis. Probability is perpendicular to the page and represented as a
contour map. The star shows the location of the maximum
probability. The two circles around the star show contours of lower
probability with the lowest probability shown by the largest
circle. These contour maps can be constructed using two dimensional
quadratic response surfaces, and the maximums can be found using
analytic maximization. In this case, and in many cases, maximum
probability is reached using different window sizes for different
variables. Moreover, the window sizes that produce the maximum
probability are likely to vary as a result of variation in the
number of tests, total testing period, the amount of cancer
progression and the curvature of the PSA trend, the functional form
chosen, and a variety of other variables and circumstances.
[0168] FIG. 33 shows an exemplary concept of maximum probability
and window sizes in a 3D situation and demonstrates its function in
4D or higher situations.
[0169] Embodiments of the present invention extend the capabilities
of dynamic screening. The capabilities relate to multiple benign
conditions as well as progressing cancer. Included are descriptions
covering temporary benign conditions, long-term conditions, both
benign and cancer, and tuning distributions using long-term
conditions as the example.
[0170] PSA and free PSA tests can have results that are greater
than or smaller than their predicted trend values. Dynamic
screening may label them anomalous and excludes them from
subsequent trend estimation if their deviations, including the
ratio free PSA to PSA, exceed certain tolerance ranges. Anomalous
results with PSA values substantially below the trend are rare and
may be caused by a variety of situations, including test error or
test recording error. Anomalous results with PSA values
substantially above the trend are more frequent and may be caused
by one or more benign conditions. Dynamic screening estimates the
probability of these benign conditions using Bayesian
processes.
Plurality of Medical Conditions
[0171] In an embodiment, capabilities are added to dynamic
screening to allow the calculation of the probability of benign
prostate conditions. Two exemplary conditions include, but are not
limited to, inflammation prostatitis and infection prostatitis
along with a category for other temporary conditions. FIG. 34 shows
the probability of each of these two temporary benign conditions
for anomalous test results over time for a man. These probabilities
can be used to inform decisions about imaging, testing and
treatment of possible conditions. Anomalous test results with PSA
values below the trend are indicated by gray bars below the
horizontal axis.
[0172] Three similar Bayes processes can be used to calculate the
probability of the prostate conditions: inflammation prostatitis,
infection prostatitis and other temporary conditions. The process
for calculating the probability of progressing cancer has been
disclosed previously. In an embodiment, the Bayes process uses
three elements: the prior probability of the condition, the
probability of the observed trend values and the incremental change
from them conditional on all conditions and the probability of the
observed trend values and the incremental change from them
conditional on the absence of the condition but with all other
conditions possible. Prior probabilities may be a function of age,
race, genetics, demographics, past experience with the conditions
and other considerations as shown in FIG. 35.
[0173] Temporary conditions of the prostate are partitioned into 7
different condition combinations that are composed of three
different prostate conditions as shown in FIG. 36. This partition
allows the use of an extension of Bayes theorem for a partition of
the event space--all relevant long-term prostate conditions in this
case.
[0174] Definitions [0175] Xi=Any one of several prostate
conditions, such as O, I, I. [0176] Xj=One specific prostate
condition, such as O or I or I. [0177] Yi=Any one of several
condition partitions, such as O, OI, OII. [0178] Yj=One specific
condition partition, such as O or OI or OII. [0179] Pr(Yj)=Prior
probability of condition partition Y. [0180] P(R|Yj=Conditional
probability of results (R) given condition partition) Y. [0181]
P(Yj|R=Conditional probability of condition partition Y given
results) (R). [0182] P(Xj|R=Conditional probability of condition X
given results (R).
[0183] Equations
P ( Yj | R ) = Pr ( Yj ) * P ( R | Yj ) .SIGMA. Pr ( Yi ) * P ( R |
Yi ) summed over all Yis , ##EQU00001##
for example:
P ( OII | R ) = Pr ( OII ) * P ( R | OII ) .SIGMA. Pr ( Yi ) * P (
R | Yi ) summed over all Yis , ##EQU00002##
where:
.SIGMA. Pr ( Yi ) * P ( R | Yi ) summed over all Yis = Pr ( O ) * P
( R | O ) + Pr ( I ) * P ( R | I ) + Pr ( I ) * P ( R | I ) + Pr (
OI ) * P ( R | OI ) + Pr ( OI ) * P ( R | OI ) + Pr ( II ) * P ( R
| II ) + Pr ( OII ) * P ( R | OII ) ##EQU00003##
[0184] P(Xj|R)=.SIGMA. P(Yj|R) summed over all Y is that contain
Xj, for example:
P ( I | R ) = P ( I | R ) + P ( OI | R ) + P ( II | R ) + P ( OII |
R ) ##EQU00004##
[0185] The probability generator for all temporary prostate
conditions consolidates output from three separate probability
generators: inflammation prostatitis, infection prostatitis and
other temporary conditions as shown in FIG. 37 and FIG. 38. Total
values are stored from iterations of the Monte Carlo process for
four variables: PSA and PSA increment from the trend, and free PSA
and free PSA increment from the trend. Ratios are calculated for
free PSA % (=free PSA/PSA) and free PSA Incrment % (=free PSA
increment/PSA increment). Other probability generators are similar
with one module removed. For example, the other condition generator
starts with the all temporary prostate conditions generator and
removes the other conditions generator.
[0186] The probability distributions of each prostate condition can
be affected by past medical experience with the conditions, and the
results of imaging, tests, treatment and other medical procedures
as shown in FIG. 39. For example, prostatic secretions can be
cultured for bacterial infections. The results can affect the
probability distributions produced by the infection prostatitis
module. In a similar way, treatment with antibiotics can affect PSA
levels. The outcome can affect the distributions produced by the
infection prostatitis module. For example, a negative bacterial
culture and no impact from antibiotic treatment may reduce the
probability of infection prostatitis and increase the probability
of inflammation and the probability of other conditions. In
contrast, a positive bacterial culture and/or beneficial impact of
antibiotic treatment may increase the probability of infection
prostatitis to a high level and reduce the probability of
inflammation and the probability of other conditions. Examples of
this are shown in as shown in FIG. 40.
[0187] In an embodiment, other clinical conditions PSA increment is
the product of the other conditions leak rate increment, drawn from
the other conditions LI % distribution, and trend PSA from the PSA
module as shown in FIG. 41. Temporary PSA is the sum of trend PSA
from the PSA module and PSA increment. In other conditions, free
PSA increment is the product of the other conditions free PSA %,
drawn from the other conditions fPSA % distribution (which may be
influenced by the healthy and BPH fPSA % s), and calculated PSA
increment. Temporary free PSA is the sum of trend free PSA from the
free PSA module and free PSA increment.
[0188] In an embodiment, inflammation PSA increment is the product
of the inflammation leak rate increment, drawn from the
inflammation LI % distribution, and trend PSA from the PSA module
as shown in FIG. 41. Temporary PSA is the sum of trend PSA from the
PSA module and PSA increment. Inflammation free PSA increment is
the product of the inflammation free PSA %, drawn from the
inflammation fPSA % distribution (which may be influenced by the
healthy and BPH fPSA % s), and calculated PSA increment. Temporary
free PSA is the sum of trend free PSA from the free PSA module and
free PSA increment.
[0189] In another embodiment, infection PSA increment is the
product of the infection leak rate increment, drawn from the
infection LI % distribution, and trend PSA from the PSA module as
shown in FIG. 41. Temporary PSA is the sum of trend PSA from the
PSA module and PSA increment. Infection free PSA increment is the
product of the infection free PSA %, drawn from the infection fPSA
% distribution (which may be influenced by the healthy and BPH fPSA
% s), and calculated PSA increment. Temporary free PSA is the sum
of trend free PSA from the free PSA module and free PSA
increment.
[0190] A total and calculation module can consolidate output from
the separate probability generators for the three temporary
prostate conditions: other temporary conditions, inflammation
prostatitis, and infection prostatitis. Values are totaled for four
variables: PSA and PSA increment, and free PSA and free PSA
increment. Ratios are calculated for free PSA % (=free PSA/PSA) and
free PSA increment % (=free PSA increment/PSA increment).
[0191] The graph in FIG. 42 shows an example of how the probability
of the presence of infection (P %) for a man tends to increase with
age and past history of infection. The more a man has had past
infections the more likely he is to have one now.
[0192] The probability density for an infection leak increment
percent (LI %) can depend on past experience as shown in FIG. 43. A
man with no history of infections will have a declining population
based distribution, shown in light gray. However, one or many
infections with high LI % s will shift the distributions to higher
peaks at larger LI % s, as shown by the dark gray and black
distributions.
[0193] The probability density for free PSA % (fPSA %) can also
depend on past experience as shown in FIG. 44. A man with no
history of infections will have a low and broad population based
distribution, shown in light gray. However, one or many infections
with very low fPSA % s will shift the distributions to higher peaks
at smaller fPSA % s, as shown by the dark gray and black
distributions.
[0194] In an aspect, an elevated or increasing PSA trend is an
indication that a long-term condition may be affecting the
prostate. Dynamic screening can estimate the probability of these
conditions using Bayesian processes.
[0195] In an embodiment, probability of other long-term prostate
conditions, in addition to progressing cancer, can be calculated.
For example, long-term conditions considered include, but are not
limited to, volume growth due to BPH, inflammation prostatitis and
infection prostatitis. The exemplary graph in FIG. 45 shows how the
probability of each of these three benign conditions can change
over time for a man. These probabilities can be used to inform
decisions about imaging, testing and treatment of possible
conditions.
[0196] In another embodiment, four similar Bayes processes are used
to calculate the probability of the prostate conditions: volume
growth due to BPH, inflammation prostatitis, infection prostatitis
and progressing cancer as shown in FIG. 46. The process for
calculating the probability of progressing cancer has been
disclosed previously. The Bayes process uses three elements: the
prior probability of the condition, the probability of the observed
trend values conditional on all conditions and the probability of
the observed trend values conditional on the absence of the
condition but with all other conditions possible. Prior
probabilities may be a function of age, race, genetics,
demographics and other considerations.
[0197] The status of the prostate is partitioned into 16 different
condition combinations that are composed of five different prostate
conditions as shown in FIG. 47. The condition combinations are
mutually exclusive and collectively exhaustive. This partition
allows the use of an extension of Bayes theorem for a partition of
the event space--all relevant long-term prostate conditions in this
case.
[0198] Definitions [0199] Xi=Any one of several medical conditions,
such as H, V, C. [0200] Xj=One specific condition, such as H or V
or C. [0201] Yi=Any one of several condition partitions, such as H,
HV, HVI/C. [0202] Yj=One specific condition partition, such as H or
HV or HVI/C. [0203] Pr(Yj)=Prior probability of condition partition
Y. [0204] P(R|Yj)=Conditional probability of results (R) given
condition partition Y. [0205] P(Yj|R)=Conditional probability of
condition partition Y given results (R). [0206] P(Xj|R)=Conditional
probability of condition X given results (R).
[0207] Equations
P ( Yj | R ) = Pr ( Yj ) * P ( R | Yj ) .SIGMA. Pr ( Yi ) * P ( R |
Yi ) summed over all Yis , ##EQU00005##
for example:
P ( HVC | R ) = Pr ( HVC ) * P ( R | HVC ) .SIGMA. Pr ( Yi ) * P (
R | Yi ) summed over all Yis , ##EQU00006##
where:
.SIGMA. Pr ( Yi ) * P ( R | Yi ) summed over all Yis = Pr ( H ) * P
( R | H ) + Pr ( HV ) * P ( R | HV ) + Pr ( HI ) * P ( R | HI ) +
Pr ( HI ) * P ( R | HI ) + Pr ( HC ) * P ( R | HC ) + Pr ( HVI ) *
P ( R | HVI ) + Pr ( HVI ) * P ( R | HVI ) + Pr ( HVC ) * P ( R |
HVC ) + Pr ( HII ) * P ( R | HII ) + Pr ( HIC ) * P ( R | HIC ) +
Pr ( HIC ) * P ( R | HIC ) + Pr ( HVII ) * P ( R | HVII ) + Pr (
HVIC ) * P ( R | HVIC ) + Pr ( HVIC ) * P ( R | HVIC ) + Pr ( HIIC
) * P ( R | HIIC ) + Pr ( HVIIC ) * P ( R | HVIIC )
##EQU00007##
[0208] P(Xj|R)=.SIGMA. P(Yj|R) summed over all Yis that contain Xj,
for example:
P ( H | R ) = P ( H | R ) ##EQU00008## P ( V | R ) = P ( HV | R ) +
P ( HVI | R ) + P ( HVI | R ) + P ( HVC | R ) + P ( HVII | R ) + P
( HVIC | R ) + P ( HVIC | R ) + P ( HVIIC | R ) ##EQU00008.2## P (
C | R ) = P ( HC | R ) + P ( HVC | R ) + P ( HIC | R ) + P ( HIC |
R ) + P ( HVIC | R ) + P ( HVIC | R ) + P ( HIIC | R )
##EQU00008.3##
[0209] In an aspect of the invention, a probability generator for
all prostate conditions consolidates output from five exemplary
separate probability generators for a healthy prostate and the four
prostate that include without limitation: volume growth due to BPH,
inflammation prostatitis, infection prostatitis and progressing
cancer as shown in FIG. 48, FIG. 49, and FIG. 50. Total values are
stored from iterations of the Monte Carlo process for six exemplary
variables that include without limitation: prostate volume and
volume velocity, PSA and PSA velocity, and free PSA and free PSA
velocity. Ratios can be calculated for free PSA % (=free PSA/PSA)
and free PSA velocity % (=free PSA velocity/PSA velocity). In an
embodiment, other probability generators can be similar with one
module removed. For example, the no BPH volume growth generator
starts with the all prostate conditions generator and removes the
BPH volume growth generator.
[0210] The probability distributions of each prostate condition can
be affected by past experience and the results of imaging, tests,
treatment and other medical procedures as shown in FIG. 51. For
example, the prostate can be imaged using ultrasound or MRI
equipment and its volume can be measured from the images. This
measurement constrains the distributions of prostate volume, PSA
and free PSA. For example, prostatic secretions can be cultured for
bacterial infections. The results will affect the probability
distributions produced by the infection prostatitis module. In a
similar way, treatment with antibiotics can affect PSA levels. The
outcome can affect the distributions produced by the infection
prostatitis module. For example, a negative bacterial culture and
no impact from antibiotic treatment will reduce the probability of
infection prostatitis and increase the probability of other
conditions, including progressing cancer. In contrast, a positive
bacterial culture and/or beneficial impact of antibiotic treatment
will increase the probability of infection prostatitis to a high
level and reduce the probability of other conditions, including
progressing cancer.
[0211] The flow charts in FIG. 52 show an embodiment of the no
cancer probability generators. The small gray boxes show the
probability distributions from which draws are made during each
Monte Carlo iteration. In an embodiment, healthy prostate volume
can be drawn from the Vol distribution. PSA is the product of PSA
density, drawn from the healthy PSAD distribution, and the healthy
prostate volume draw. Free PSA is the product of the free PSA %,
drawn from the fPSA % distribution, and calculated PSA. In another
embodiment, BPH volume is drawn from the Vol distribution, which
may be influenced by the healthy Vol distribution as shown by the
dotted line. BPH volume velocity is drawn from the VolVel
distribution, which may be influenced by the healthy Vol
distribution and the BPH Vol distribution, as shown by the dotted
lines. PSA is the product of PSA density, drawn from the BPH PSAD
distribution (which may be influenced by the healthy PSAD
distribution as shown by the A connector), and the BPH prostate
volume draw. PSA velocity is the product of PSA density, drawn from
the BPH PSAD distribution (which may be influenced by the healthy
PSAD distribution as shown by the A connector), and the BPH
prostate volume velocity draw. Free PSA is the product of the free
PSA %, drawn from the BPH fPSA % distribution (which may be
influenced by the healthy fPSA % distribution as shown by the B
connector), and calculated PSA. Free PSA velocity is the product of
the free PSA %, drawn from the BPH fPSA % distribution (which may
be influenced by the healthy fPSA % distribution as shown by the B
connector), and calculated PSA velocity. A summation module can add
healthy prostate and BPH volume growth variables: volume, volume
velocity, PSA, PSA velocity, free PSA and free PSA velocity.
[0212] In an embodiment, inflammation prostatitis PSA is the
product of the inflammation leak rate, drawn from the inflammation
L % distribution, and Sum PSA from the summation module. PSA
velocity has two sources. First, PSA velocity is the product of the
leak rate velocity, drawn from the LV % distribution, which may be
influenced by L %, and calculated inflammation PSA. Second, PSA
velocity is the product of the leak rate, drawn from the
inflammation L % distribution, and Sum PSA velocity from the
summation module. Both sources of PSA velocity are summed in the
module. Inflammation prostatitis free PSA can be the product of
inflammation free PSA %, drawn from the inflammation fPSA %
distribution (which may be influenced by the healthy and BPH fPSA %
s), and calculated inflammation PSA. Free PSA velocity has two
sources. First, free PSA velocity is the product of the free PSA %,
drawn from the fPSA % distribution, and calculated inflammation PSA
velocity (which came from the leak rate velocity %. Second, free
PSA velocity is the product of the free PSA %, drawn from the
inflammation fPSA % distribution, and calculated inflammation PSA
velocity (which came from PSAV caused by volume velocity). Both
sources of free PSA velocity are summed in the module.
[0213] In an embodiment, infection prostatitis PSA is the product
of the infection leak rate, drawn from the infection L %
distribution, and Sum PSA from the summation module as shown in
FIG. 56. PSA velocity has two sources. First, PSA velocity is the
product of the leak rate velocity, drawn from the LV %
distribution, which may be influenced by L %, and calculated
infection PSA. Second, PSA velocity is the product of the leak
rate, drawn from the infection L % distribution, and Sum PSA
velocity from the summation module. Both sources of PSA velocity
are summed in the module. Infection prostatitis free PSA can be the
product of infection free PSA %, drawn from the infection fPSA %
distribution (which may be influenced by the healthy and BPH fPSA %
s), and calculated infection PSA. Free PSA velocity has two
sources. First, free PSA velocity is the product of the free PSA %,
drawn from the fPSA % distribution, and calculated infection PSA
velocity (which came from the leak rate velocity %). Second, free
PSA velocity is the product of the free PSA %, drawn from the
infection fPSA % distribution, and calculated infection PSA
velocity (which came from PSAV caused by volume velocity). Both
sources of free PSA velocity are summed in the module.
[0214] In an example, a total and calculation module consolidates
output from the separate probability generators for the four benign
prostate conditions: healthy prostate, volume growth due to BPH,
inflammation prostatitis, infection prostatitis and progressing
cancer. Values are totaled for six variables: prostate volume and
volume velocity, PSA and PSA velocity, and free PSA and free PSA
velocity. Ratios are calculated for free PSA % (=free PSA/PSA) and
free PSA velocity % (=free PSA velocity/PSA velocity).
[0215] In an embodiment, a healthy prostate module has three
distributions for Monte Carlo draws: Vol, PSAD and fPSA % as shown
in FIG. 53. Vol is the healthy volume distribution from which a
man's volume is drawn in each Monte Carlo iteration. It is affected
by age, demographics and past volume measurements. An example for
Vol is shown with a mean of 28.0 ccs and standard deviation of 4.3
ccs. PSAD is the healthy PSA density distribution from which a
man's PSA density is drawn in each Monte Carlo iteration. It is
affected by age, demographics and past PSA trends and volume
measurements. An example for PSAD is shown with a mean of 0.035
PSA/cc and standard deviation of 0.008. fPSA % is the healthy free
PSA % distribution from which a man's fPSA % is drawn in each Monte
Carlo iteration. It is affected by age, demographics and past free
PSA and PSA trends. An example for fPSA % is shown with a mean of
28% and standard deviation of 7%.
[0216] In an embodiment, the BPH volume growth module has four
distributions for Monte Carlo draws: Vol, VolVel, PSAD and fPSA %
as shown in FIG. 54. Vol is the BPH volume multiplier distribution
from which a man's volume increase ratio to his healthy volume is
drawn in each Monte Carlo iteration. Vol has two parts: the
presence probability, P %, and the distribution of its values. P %
is the binary probability that BPH is present and has caused an
increase in the PSA trend. It is affected by age, demographics, the
healthy volume draw and past volume measurements. P % is set equal
to 100% for the certain module in the bold box. The distribution
density function is likely to decline in density with increasing
Vol. It may change based on past experience. VolVel is the BPH
volume velocity distribution from which a man's volume velocity is
drawn in each Monte Carlo iteration. VolVel is the annual rate of
increase in prostate volume due to BPH. VolVel has two parts: the
presence probability, P %, and the distribution of its values. P %
is either 1 or 0 based on the P % draw in the Vol module. P % is
set equal to 100% for the certain module in the bold box. The
distribution density function is likely to decline in density with
increasing VolVel. It may change based on age, demographics, drawn
values for healthy and BPH volumes, and volume measurements. PSAD
is the BPH volume PSA density distribution from which a man's BPH
PSA density is drawn in each Monte Carlo iteration. It is affected
by age, demographics, drawn healthy PSAD and past PSA trends and
volume measurements. An example for PSAD is shown with a mean equal
to the BPH % times the healthy PSAD draw and standard deviation of
CV % times the mean. BPH % tends to roughly 100% because BPH
density is similar to healthy density. fPSA % is the BPH volume
growth free PSA % distribution from which a man's fPSA % is drawn
in each Monte Carlo iteration. It is affected by age, demographics
and past free PSA and PSA trends. An example for fPSA % is shown.
It has a mean of BPH % times the drawn healthy fPSA %. Typically,
BPH % is greater than 100% because BPH volume growth tends to
increase free PSA %. CV % may be relatively small.
[0217] In an embodiment, the inflammation prostatitis module has
three distributions for Monte Carlo draws: L %, LV % and fPSA % as
shown in FIG. 55. L % is the leak rate percent and has two parts:
the presence probability, P %, and the distribution of its values.
P % is the binary probability that inflammation is present and has
caused an increase in the PSA trend. It is based on age,
demographics and past experience with inflammation. P % is set
equal to 100% for the certain module in the bold box. The
distribution density function is likely to decline in density with
increasing L %. It may change based on past experience. LV % is the
leak rate velocity percent and describes how increasing
inflammation increases the amount of PSA over time by leaking
higher percentages of PSA. LV % has two parts: the presence
probability, P %, and the distribution of its values. P % is either
1 or 0 based on the P % draw in the L % module. P % is set equal to
100% for the certain module in the bold box. The distribution
density function is likely to decline in density with increasing LV
%. It may change based on past experience. An example for fPSA % is
shown with the mean equal to Inflammation % times the ratio of
healthy plus BPH free PSA to PSA. Typically, Inflammation % is
roughly 100% because inflammation tends to increase the amount of
PSA leakage without changing the free PSA %
substantially--resulting in a relatively small CV %.
[0218] In an embodiment, the infection prostatitis module has three
distributions for Monte Carlo draws: L %, LV % and fPSA % as shown
in FIG. 56. L % is the leak rate percent and has two parts: the
presence probability, P %, and the distribution of its values. P %
is the binary probability that infection is present and has caused
an increase in the PSA trend. It is based on age, demographics and
past experience with infection. P % is set equal to 100% for the
certain module in the bold box. The distribution density function
is likely to decline in density with increasing L %. It may change
based on past experience. LV % is the leak rate velocity percent
and describes how increasing infection increases the amount of PSA
over time by leaking higher percentages of PSA. LV % has two parts:
the presence probability, P %, and the distribution of its values.
P % is either 1 or 0 based on the P % draw in the L % module. P %
is set equal to 100% for the certain module in the bold box. The
distribution density function is likely to decline in density with
increasing LV %. It may change based on past experience. An example
for fPSA % is shown with the mean equal to Infection % times the
ratio of healthy plus BPH free PSA to PSA. Typically, Infection %
is much less than 100% because infection tends to decrease the
amount of PSA leakage while decreasing free PSA % substantially. CV
% may be relatively large.
Model Tuning
[0219] An enormous amount of data can be needed to define all the
underlying distributions completely. In practice, the amount of
data needed to define the distributions is not practical to obtain.
Therefore, an iterative process is needed to tune the parameters of
the underlying distributions so that known relationships are
satisfied and the overall distributions conform to population
studies.
[0220] In an aspect, an iterative Monte Carlo process generates
multi-dimensional distributions for men of a given age without
prostate cancer. Static parts of the distribution (no velocities as
shown below) can be validated against available distributions. For
example, the Center for Disease Control has published distributions
of PSA, free PSA and free PSA % for U.S. men in ten year age ranges
from age forty to age eighty and above, and the Mayo Clinic has
published prostate volume and PSA distributions for men from age
forty to age eighty in Olmsted County, MN. Distributions like these
constrain the overall distributions generated by the Monte Carlo
process. Details of these distributions and other medical studies
constrain the results of the specific probability generators and
the relationships among them. For example, the CDC distributions
show a significant decline in free PSA % for higher levels of PSA.
This result strongly suggests that infection prostatitis accounts
for an increasing proportion of higher PSA results because it is
the only benign condition that produces free PSA in a percent that
is significantly lower than the other benign conditions. Exemplary
tuning of parameters and validation of detailed distributions is
demonstrated in as shown in FIG. 58.
[0221] In an embodiment, the first step of a tuning process of the
invention is to tune the no cancer static distribution for a given
age (t=0), such as age 55. No velocities need be calculated for
this static distribution as shown in FIG. 57. Unused modules are
shown as blank in the figure. Starting parameters for all
underlying distributions, the gray boxes, are chosen consistent
with known relationships, and an iterative Monte Carlo process is
run. The resulting multi-dimensional distribution is compared to
the population distribution. New parameters for all underlying
distributions are chosen consistent with known relationships, and
an iterative Monte Carlo process is run again. The resulting
multi-dimensional distribution is compared to the population
distribution. Over many cycles through this process the
multi-dimensional distribution converges on the population
distribution while maintaining known relationships to the extent
possible. Advanced solution algorithms may be used to speed the
convergence process. This tuning process is repeated for a range of
ages, such as age 45, 55, 65 and 75.
[0222] In another embodiment of a tuning process of the invention,
the next step is to tune the velocity distribution parameters.
Static results for a given year plus the changes caused by
velocities accumulated over a ten year period should yield the
static distribution ten years later. An iterative Monte Carlo
process using static and velocity parameters generates
multi-dimensional distributions for men ten years later without
prostate cancer. Static parts of the distribution can be validated
against available distributions. For example, the Center for
Disease Control has published distributions of PSA, free PSA and
free PSA % for U.S. men in ten year age ranges from age forty to
age eighty and above, and the Mayo Clinic has published prostate
volume and PSA distributions for men from age forty to age eighty
in Olmsted County, Minn. Distributions like these constrain the
overall distributions generated by the Monte Carlo process. Details
of these distributions and other medical studies constrain the
results of the specific probability generators and the
relationships among them.
[0223] In another embodiment, a tuning process involves using the
parameters for the no cancer static distribution for a given age
(t=0), such as age 55. A second step is to tune velocity parameters
to achieve the no cancer static distribution for ten years later
(t=10), such as age 65 as shown in FIG. 59 and FIG. 60. Starting
parameters for all underlying velocity distributions are chosen
consistent with known relationships, and an iterative Monte Carlo
process is run. The resulting multi-dimensional static distribution
for ten years later is compared to the population distribution. New
parameters for all underlying velocity distributions are chosen
consistent with known relationships, and an iterative Monte Carlo
process is run again. The resulting multi-dimensional static
distribution is compared to the population distribution. Over many
cycles through this process the multi-dimensional static
distribution converges on the population distribution while
maintaining known relationships to the extent possible. Advanced
solution algorithms may be used to speed the convergence process.
This tuning process is repeated for a range of ages.
Integrated Health Systems
[0224] In another aspect, a medical information system for
assessing a disease of a subject is provided that comprises: an
input device for receiving subject data; a processor that assesses
a probability of said data relating to historical data; a storage
unit in communication with the processor having a database for: (i)
storing the subject data; (ii) storing historical data related to
the disease; and an output device that transmits information
relating to the probability of said data relating to historical
data to an end user.
[0225] Also provided herein is a method for assessing a disease in
a subject comprising: collecting data from the subject
corresponding to a biomarker for the disease at at least two
different times, wherein the data corresponding to the at least two
different times form a trend; exporting said data for manipulation
of said data by executing a method herein; and importing the
results of said manipulation to an end user. For example, data is
collected at a first location, such as a hospital, the data is
exported to a second location, such as a remote server in any
remote location, where a method of the invention is executed to
obtain information regarding the disease in a subject, and then the
information is imported from the remote location back to the first
location, such as the point-of-care in the hospital, or the
information is imported to a third location, such as a
database.
[0226] It is further noted that the systems and methods may be
implemented on various types of computer architectures, such as for
example on a networked system or in a client-server configuration,
or in an application service provider configuration, on a single
general purpose computer or workstation. The systems and methods
may include data signals conveyed via networks (for example, local
area network, wide area network, internet, combinations thereof),
fiber optic medium, carrier waves, and wireless networks for
communication with one or more data processing devices. The data
signals can carry any or all of the data disclosed herein (for
example, user input data, the results of the analysis to a user)
that is provided to or from a device.
[0227] Additionally, the methods and systems described herein may
be implemented on many different types of processing devices by
program code comprising program instructions that are executable by
the device processing subsystem. The software program instructions
may include source code, object code, machine code, or any other
stored data that is operable to cause a processing system to
perform methods described herein.
[0228] The systems' and methods' data (for example, associations,
mappings) may be stored and implemented in one or more different
types of computer-implemented ways, such as different types of
storage devices and programming constructs (for example, data
stores, RAM, ROM, Flash memory, flat files, databases, programming
data structures, programming variables, IF-THEN (or similar type)
statement constructs). It is noted that data structures describe
formats for use in organizing and storing data in databases,
programs, memory, or other computer-readable media for use by a
computer program.
[0229] The systems and methods may be provided on many different
types of computer-readable media including computer storage
mechanisms (for example, CD-ROM, diskette, RAM, flash memory,
computer's hard drive, magnetic tape, and holographic storage) that
contain instructions (for example, software) for use in execution
by a processor to perform the methods' operations and implement the
systems described herein.
[0230] The computer components, software modules, functions, data
stores and data structures described herein may be connected
directly or indirectly to each other in order to allow the flow of
data needed for their operations. It is also noted that the meaning
of the term module includes but is not limited to a unit of code
that performs a software operation, and can be implemented for
example as a subroutine unit of code, or as a software function
unit of code, or as an object (as in an object-oriented paradigm),
or as an applet, or in a computer script language, or as another
type of computer code. The software components and/or functionality
may be located on a single computer or distributed across multiple
computers depending upon the situation at hand.
[0231] In yet another aspect, a computer readable medium is
provided including computer readable instructions, wherein the
computer readable instructions instruct a processor to execute step
a) of the methods described above. The instructions can operate in
a software runtime environment.
[0232] In yet another aspect, a data signal is provided that can be
transmitted using a network, wherein the data signal includes said
posterior probability calculated in a step of the methods described
above. The data signal can further include packetized data that is
transmitted through wired or wireless networks.
[0233] In an aspect, a computer readable medium comprises computer
readable instructions, wherein the instructions when executed carry
out a calculation of the probability of a medical condition in a
patient based upon data obtained from the patient corresponding to
at least one biomarker. The computer readable instructions can
operate in a software runtime environment of the processor. In an
embodiment, a software runtime environment provides commonly used
functions and facilities required by the software package. Examples
of a software runtime environment include, but are not limited to,
computer operating systems, virtual machines or distributed
operating systems. As will be appreciated by those of ordinary
skill in the art, several other examples of runtime environment
exist. The computer readable instructions can be packaged and
marketed as a software product or part of a software package. For
example, the instructions can be packaged with an assay kit for
PSA.
[0234] The computer readable medium may be a storage unit. It is
appreciated by those skilled in the art that computer readable
medium can also be any available media that can be accessed by a
server, a processor, or a computer. The computer readable medium
can be incorporated as part of the computer-based system, and can
be employed for a computer-based assessment of a medical
condition.
[0235] In an embodiment, the calculation of a probability can be
carried out on a computer system. The computer system can comprise
any or all of the following: a processor, a storage unit, software,
firmware, a network communication device, a display, a data input,
and a data output. A computer system can be a server. A server can
be a central server that communicates over a network to a plurality
of input devices and/or a plurality of output devices. A server can
comprise at least one storage unit, such as a hard drive or any
other device for storing information to be accessed by a processor
or external device, wherein the storage unit can comprise one or
more databases. In an embodiment, a database can store hundreds to
millions of data points corresponding to a biomarker from hundreds
to millions of subjects. A storage unit can also store historical
data read from an external database or as input by a user. In an
embodiment, a storage unit stores data received from an input
device that is communicating or has communicated with the server. A
storage unit can comprise a plurality of databases. In an
embodiment, each of a plurality of databases corresponds to each of
a plurality of biomarkers. In another embodiment, each of a
plurality of databases corresponds to each of a plurality of
possible medical conditions of a subject. An individual database
can also comprise information for a plurality of possible medical
conditions or a plurality of biomarkers or both. Further, a
computer system can comprise multiple servers.
[0236] A processor can access data from a storage unit or from an
input device to perform a calculation of an output from the data. A
processor can execute software or computer readable instructions as
provided by a user, or provided by the computer system or server.
The processor may have a means for receiving patient data directly
from an input device, a means of storing the subject data in a
storage unit, and a means for processing data. The processor may
also include a means for receiving instructions from a user or a
user interface. The processor may have memory, such as random
access memory, as is well known in the art. In one embodiment, an
output that is in communication with the processor is provided.
[0237] After performing a calculation, a processor can provide the
output, such as from a calculation, back to, for example, the input
device or storage unit, to another storage unit of the same or
different computer system, or to an output device. Output from the
processor can be displayed by data display. A data display can be a
display screen (for example, a monitor or a screen on a digital
device), a print-out, a data signal (for example, a packet), an
alarm (for example, a flashing light or a sound), a graphical user
interface (for example, a webpage), or a combination of any of the
above. In an embodiment, an output is transmitted over a network
(for example, a wireless network) to an output device. The output
device can be used by a user to receive the output from the
data-processing computer system. After an output has been received
by a user, the user can determine a course of action, or can carry
out a course of action, such as a medical treatment when the user
is medical personnel. In an embodiment, an output device is the
same device as the input device. Example output devices include,
but are not limited to, a telephone, a wireless telephone, a mobile
phone, a PDA, a flash memory drive, a light source, a sound
generator, a fax machine, a computer, a computer monitor, a
printer, an iPOD, and a webpage. The user station may be in
communication with a printer or a display monitor to output the
information processed by the server.
[0238] A client-server, relational database architecture can be
used in embodiments of the invention. A client server architecture
is a network architecture in which each computer or process on the
network is either a client or a server. Server computers are
typically powerful computers dedicated to managing disk drives
(file servers), printers (print servers), or network traffic
(network servers). Client computers include PCs (personal
computers) or workstations on which users run applications, as well
as example output devices as disclosed herein. Client computers
rely on server computers for resources, such as files, devices, and
even processing power. In some embodiments of the invention, the
server computer handles all of the database functionality. The
client computer can have software that handles all the front-end
data management and can also receive data input from users.
[0239] A database can be developed for a medical condition in which
relevant information is filtered or obtained over a communication
network (for example, the internet) from one or more data sources,
such as a public remote database, an internal remote database, and
a local database. A public database can include online sources of
free data for use by the general public, such as, for example,
databases supplied by the U.S. Department of Health and Human
Services. For example, an internal database can be a private
internal database belonging to particular hospital, or a SMS
(Shared Medical system) for providing data. A local database can
comprise, for example, biomarker data relating to a medical
condition. The local database may include data from a clinical
trial. It may also include data such as blood test results, patient
survey responses, or other items from patients in a hospital.
[0240] Subject data can be stored with a unique identifier for
recognition by a processor or a user. In another step, the
processor or user can conduct a search of stored data by selecting
at least one criterion for particular patient data. The particular
patient data can then be retrieved.
[0241] In an example, a subject or medical professional enters
medical data from a biomarker assay into a webpage. The webpage
transmits the data to a computer system or server, wherein the data
is stored and processed. For example, the data can be stored in
databases the computer systems. Processors in the computer systems
can perform calculations comparing the input data to historical
data from databases available to the computer systems. The computer
systems can then store the output from the calculations in a
database and/or communicate the output over a network to an output
device, such as a webpage or email. After a user has received an
output from the computer system, the user can take a course of
medical action according to the output. For example, if the user is
a physician and the output is a probability of cancer above a
threshold value, the physician can then perform or order a biopsy
of the suspected tissue.
[0242] FIG. 61 demonstrates an example computer system of the
invention. A set of users can use a web browser to enter data from
a biomarker assay into a graphical user interface of a webpage. The
webpage is a graphical user interface associated with a front end
server, wherein the front end server can communicate with the
user's input device (for example, a computer) and a back end
server. The front end server can either comprise or be in
communication with a storage device that has a front-end database
capable of storing any type of data, for example user account
information, user input, and reports to be output to a user. Data
from each user (for example, biomarker values and subject profiles)
can be then be sent to a back end server capable of manipulating
the data to generate a result. For example, the back end server can
calculate a probability that a subject has a medical condition
using the data input by the user. A back end server can comprise
historical data relating to a medical condition to be evaluated, or
a plurality of medical conditions. The back end server can then
send the result of the manipulation or calculation back to the
front end server where it can be stored in a database or can be
used to generate a report. The results can be transmitted from the
front end server to an output device (for example, a computer with
a web browser) to be delivered to a user. A different user can
input the data and receive the data. In an embodiment, results are
delivered in a report. In another embodiment, results are delivered
directly to an output device that can alert a user.
[0243] In an embodiment, a method of the invention comprises
obtaining a sample from a subject, wherein the sample contains a
biomarker. The sample can be obtained by the subject or by a
medical professional. Examples of medical professionals include,
but are not limited to, physicians, emergency medical technicians,
nurses, first responders, psychologists, medical physics personnel,
nurse practitioners, surgeons, dentists, and any other obvious
medical professional as would be known to one skilled in the art.
The sample can be obtained from any bodily fluid, for example,
amniotic fluid, aqueous humor, bile, lymph, breast milk,
interstitial fluid, blood, blood plasma, cerumen (earwax), Cowper's
fluid (pre-ejaculatory fluid), chyle, chyme, female ejaculate,
menses, mucus, saliva, urine, vomit, tears, vaginal lubrication,
sweat, serum, semen, sebum, pus, pleural fluid, cerebrospinal
fluid, synovial fluid, intracellular fluid, and vitreous humour. In
an example, the sample is obtained by a blood draw, where the
medical professional draws blood from a subject, such as by a
syringe. The bodily fluid can then be tested to determine the
prevalence of the biomarker. Biological markers, also referred to
herein as biomarkers, according to the present invention include
without limitation drugs, prodrugs, pharmaceutical agents, drug
metabolites, biomarkers such as expressed proteins and cell
markers, antibodies, serum proteins, cholesterol, polysaccharides,
nucleic acids, biological analytes, biomarker, gene, protein, or
hormone, or any combination thereof. At a molecular level, the
biomarkers can be polypeptide, glycoprotein, polysaccharide, lipid,
nucleic acid, and a combination thereof.
[0244] Example biomarker assays include, but are not limited to,
DNA assays, including DNA microarrays, Southern blots, Northern
blots, ELISAs, flow cytometry, Western blots, PSA assays, and
immunoassays. The information from the assay can be quantitative
and sent to a computer system of the invention. The information can
also be qualitative, such as observing patterns or fluorescence,
which can be translated into a quantitative measure by a user or
automatically by a reader or computer system. In an embodiment, the
subject can also provide information other than biomarker assay
information to a computer system, such as race, height, weight,
age, gender, eye color, hair color, family medical history and any
other information that may be useful to the user, as would be
obvious.
[0245] Information can be sent to a computer system automatically
by a device that reads or provides the data from a biomarker assay.
In another embodiment, information is entered by a user (for
example, the subject or medical professional) into a computer
system using an input device. The input device can be a personal
computer, a mobile phone or other wireless device, or can be the
graphical user interface of a webpage. For example, a webpage
programmed in JAVA can comprise different input boxes to which text
can be added by a user, wherein the string input by the user is
then sent to a computer system for processing. The subject may
input data in a variety of ways, or using a variety of devices.
Data may be automatically obtained and input into a computer from
another computer or data entry system. Another method of inputting
data to a database is using an input device such as a keyboard,
touch screen, trackball, or a mouse for directly entering data into
a database.
[0246] In another embodiment, a system can further include a
medical test for testing said subject for said medical condition.
The medical test can be a PSA assay. In yet another embodiment, a
system can further include a medical treatment for treating said
subject for said medical condition. The medical treatment can be
selected from a group including the following: a pharmaceutical,
surgery, organ resection, and radiation therapy.
[0247] In an embodiment, a computer system comprises a storage
unit, a processor, and a network communication unit. For example,
the computer system can be a personal computer, laptop computer, or
a plurality of computers. The computer system can also be a server
or a plurality of servers. Computer readable instructions, such as
software or firmware, can be stored on a storage unit of the
computer system. A storage unit can also comprise at least one
database for storing and organizing information received and
generated by the computer system. In an embodiment, a database
comprises historical data, wherein the historical data can be
automatically populated from another database or entered by a
user.
[0248] In an embodiment, a processor of the computer system
accesses at least one of the databases or receives information
directly from an input device as a source of information to be
processed. The processor can perform a calculation on the
information source, for example, performing dynamic screening or a
probability calculation method. After the calculation the processor
can transmit the results to a database or directly to an output
device. A database for receiving results can be the same as the
input database or the historical database. An output device can
communicate over a network with a computer system of the invention.
The output device can be any device capable delivering processed
results to a user. Example output devices include, but are not
limited to, a telephone, a wireless telephone, a mobile phone, a
PDA, a flash memory drive, a light source, a sound generator, a fax
machine, a computer, a computer monitor, a printer, an iPOD, and a
webpage.
[0249] An output of a computer system may assume any form, such as
a computer program, webpage, or print-out. Any other suitable
representation, picture, depiction or exemplification may be
used.
[0250] Communication between devices or computer systems of the
invention can be any method of digital communication including, for
example, over the internet. Network communication can be wireless,
ethernet-based, fiber optic, or through fire-wire, USB, or any
other connection capable of communication as would be obvious to
one skilled in the art. In an embodiment, information transmitted
by a system or method of the invention can be encrypted by any
method as would be obvious to one skilled in the art. In the field
of medicine, or diagnostics, encryption may be necessary to
maintain privacy of the data, as well as deter theft of
information.
[0251] It is further noted that the systems and methods may include
data signals conveyed via networks (for example, local area
network, wide area network, internet), fiber optic medium, carrier
waves, wireless networks for communication with one or more data
processing or storage devices. The data signals can carry any or
all of the data disclosed herein that is provided to or from a
device.
[0252] Additionally, the methods and systems described herein may
be implemented on many different types of processing devices by
program code comprising program instructions that are executable by
the device processing subsystem. The software program instructions
may include source code, object code, machine code, or any other
stored data that is operable to cause a processing system to
perform methods described herein. Other implementations may also be
used, however, such as firmware or even appropriately designed
hardware configured to carry out the methods and systems described
herein.
[0253] The methods herein may be packaged as a computer program
product, such as the expression of an organized set of instructions
in the form of natural or programming language statements that is
contained on a physical media of any nature (for example, written,
electronic, magnetic, optical or otherwise) and that may be used
with a computer or other automated data processing system of any
nature (but preferably based on digital technology). Such
programming language statements, when executed by a computer or
data processing system, cause the computer system to act in
accordance with the particular content of the statements. Computer
program products include without limitation: programs in source and
object code and/or test or data libraries embedded in a computer
readable medium. Furthermore, the computer program product that
enables a computer system or data processing equipment device to
act in preselected ways may be provided in a number of forms,
including, but not limited to, original source code, assembly code,
object code, machine language, encrypted or compressed versions of
the foregoing and any and all equivalents.
[0254] Information before, after, or during processing can be
displayed on any graphical display interface in communication with
a computer system (for example, a server). A computer system may be
physically separate from the instrument used to obtain values from
the subject. In an embodiment, a graphical user interface also may
be remote from the computer system, for example, part of a wireless
device in communication with the network. In another embodiment,
the computer and the instrument are the same device.
[0255] An output device or input device of a computer system can
include one or more user devices comprising a graphical user
interface comprising interface elements such as buttons, pull down
menus, scroll bars, fields for entering text, and the like as are
routinely found in graphical user interfaces known in the art.
Requests entered on a user interface are transmitted to an
application program in the system (such as a Web application). In
one embodiment, a user of user device in the system is able to
directly access data using an HTML interface provided by Web
browsers and Web server of the system.
[0256] A graphical user interface may be generated by a graphical
user interface code as part of the operating system or server and
can be used to input data and/or to display input data. The result
of processed data can be displayed in the interface or a different
interface, printed on a printer in communication with the system,
saved in a memory device, and/or transmitted over a network. A user
interface can refer to graphical, textual, or auditory information
presented to a user and may also refer to the control sequences
used for controlling a program or device, such as keystrokes,
movements, or selections. In another example, a user interface may
be a touch screen, monitor, keyboard, mouse, or any other item that
allows a user to interact with a system of the invention as would
be obvious to one skilled in the art.
[0257] In yet another aspect, a method of taking a course of
medical action by a user is provided including initiating a course
of medical action based on a posterior probability delivered from
an output device to said user.
[0258] The course of medical action can be delivering medical
treatment to said subject. The medical treatment can be selected
from a group consisting of the following: a pharmaceutical,
surgery, organ resection, and radiation therapy. The pharmaceutical
can include, for example, a chemotherapeutic compound for cancer
therapy. The course of medical action can include, for example,
administration of medical tests, medical imaging of said subject,
setting a specific time for delivering medical treatment, a biopsy,
and a consultation with a medical professional.
[0259] The course of medical action can include, for example,
repeating a method described above.
[0260] A method can further include diagnosing the medical
condition of the subject by said user with said posterior
probability from said output device.
[0261] A system or method can involve delivering a medical
treatment or initiating a course of medical action. If a disease
has been assessed or diagnosed by a method or system of the
invention, a medical professional can evaluate the assessment or
diagnosis and deliver a medical treatment according to his
evaluation. Medical treatments can be any method or product meant
to treat a disease or symptoms of the disease. In an embodiment, a
system or method initiates a course of medical action. A course of
medical action is often determined by a medical professional
evaluating the results from a processor of a computer system of the
invention. For example, a medical professional may receive output
information that informs him that a subject has a 97% probability
of having a particular medical condition. Based on this
probability, the medical professional can choose the most
appropriate course of medical action, such as biopsy, surgery,
medical treatment, or no action. In an embodiment, a computer
system of the invention can store a plurality of examples of
courses of medical action in a database, wherein processed results
can trigger the delivery of one or a plurality of the example
courses of action to be output to a user. In an embodiment, a
computer system outputs information and an example course of
medical action. In another embodiment, the computer system can
initiate an appropriate course of medical action. For example,
based on the processed results, the computer system can communicate
to a device that can deliver a pharmaceutical to a subject. In
another example, the computer system can contact emergency
personnel or a medical professional based on the results of the
processing. Courses of medical action a patient can take include
self-administering a drug, applying an ointment, altering work
schedule, altering sleep schedule, resting, altering diet, removing
a dressing, or scheduling an appointment and/or visiting a medical
professional. A medical professional can be for example a
physician, emergency medical personnel, a pharmacist, psychiatrist,
psychologist, chiropractor, acupuncturist, dermatologist,
urologist, proctologist, podiatrist, oncologist, gynecologist,
neurologist, pathologist, pediatrician, radiologist, a dentist,
endocrinologist, gastroenterologist, hematologist, nephrologist,
ophthalmologist, physical therapist, nutritionist, physical
therapist, or a surgeon.
[0262] Medical professionals may take medical action when alerted
by the methods of the invention of the medical condition of a
subject. Examples of an alert include, but are not limited to, a
sound, a light, a printout, a readout, a display, an alarm, a
buzzer, a page, an e-mail, a fax alert, telephonic communication,
or a combination thereof. The alert may communicate to the user the
raw subject data, the calculated probability of the subject
data.
[0263] The medical action can be based on rules imposed by the
medical professional or the computer system. Courses of medical
action include, but are not limited to, surgery, radiation therapy,
chemotherapy, prescribing a medication, evaluating mental state,
delivering pharmaceuticals, monitoring or observation, biopsy,
imaging, and performing assays and other diagnostic tests. In an
embodiment, the course of medical action may be inaction. Medical
action also includes, but is not limited to, ordering more tests
performed on the patient, administering a therapeutic agent,
altering the dosage of an administered therapeutic agent,
terminating the administration of a therapeutic agent, combining
therapies, administering an alternative therapy, placing the
subject on a dialysis or heart and lung machine, performing
computerized axial tomography (CAT or CT) scan, performing magnetic
resonance imaging (MRI), performing a colonoscopy, administering a
pain killer, prescribing a medication. In some embodiments, the
subject may take medical action. For example, a diabetic subject
may administer a dose of insulin.
[0264] FIG. 62 illustrates a method of delivering a probability
that a subject has a medical condition to a user and using the
probability to take a course of medical action. A blood sample is
drawn from a patient by a medical professional. In other
embodiments, any method of obtaining a biomarker values from a
subject may be used as would be obvious to one skilled in the art,
such as swabs and urine tests. In FIG. 62, the sample is assayed
for a biomarker and biomarker values are generated. As described
herein, there may be many suitable methods for generating and
obtaining biomarker values. The values can then input into a
computer by a medical professional or other user, such as the
subject or an assistant. The data can then be processed by a method
of the invention to calculate the probability that a subject has
the medical condition. An output is generated and delivered to a
user on a computer monitor, for example, the output delivers the
probability of a subject having a medical condition and is display
on a personal computer or laptop of the subject's doctor. The
output can also be delivered to the subject himself or to a
different medical professional. In another embodiment, the output
is delivered to a notification system, such as an alarm or another
computer-based program. In FIG. 62, based on the output, a
physician can take a medical action as described herein. In this
example, the output initiates a medical professional writing a
prescription.
[0265] FIG. 63 illustrates a course of events related to the
invention. Data regarding a biomarker corresponding to a medical
condition from a patient are stored on a USB flash drive storage
device. Data are input into a computer system and data are
processed by a calculation method of the invention. For example,
the computer system can be a server that receives data from
multiple input devices and can distribute results of a calculation
method to a plurality of output devices. In the example in FIG. 63,
the results of the calculation method are a probability that a
patient has a medical condition. The results delivered to the
output device can also be suggestions of courses of medical action,
reports based on the biomarker data, or warning or notification of
the status of the patient and/or calculation. FIG. 63 also
demonstrates displaying a probability of the medical condition of
the subject on an output device such as an iPOD. In this example,
after reviewing the output, a user decides the course of medical
action is a patient needs to obtain an MR image.
[0266] FIG. 64 illustrates another example practice of the
invention. A sample is taken from a patient by a syringe and the
sample is analyzed for a biomarker using a microscope to obtain a
biomarker value corresponding to a medical condition. Using a
graphical user interface, such as a website, a user can enter the
results of the analysis into the graphical user interface, or input
device. The result of the biomarker analysis is transmitted from an
input device, such as a laptop computer and the biomarker values
are processed using a calculation method the invention in a server
of the invention. A probability of the subject from which the
biomarker values were obtained is output to a printout from a
printer to a user, such as the subject's physician. In this
example, the physician may take a course of medical action that
comprises delivering a medical treatment, such as performing an
invasive surgical procedure, such as a biopsy, based on results of
the calculation.
Business Methods
[0267] In another aspect, a business method is disclosed that
comprises: receiving a first value of at least one biomarker of a
subject; calculating a first plurality of posterior probabilities
of the occurrence of a plurality of medical conditions of said
subject with a computer system using said a first value; delivering
said first plurality of posterior probabilities to a user;
receiving a second value of at least one biomarker of a subject and
a result of a course of medical action taken by said user based
upon said delivery of said first plurality of posterior
probabilities; calculating a second plurality of posterior
probabilities of the occurrence of a plurality of medical
conditions of said subject with said computer system using said a
second value and said result of a course of the medical action; and
delivering said second plurality of posterior probabilities to said
user. In an embodiment, the first or second values are received
from a user, such as a user selected from the group consisting of
the following: a physician, a health care provider, a pharmacy, an
insurance company, and the subject. A first or second value can
also be received from said user through a webpage or an electronic
device or an assay device.
[0268] In another embodiment, the first or second values are
received from a device, such as a device selected from the group
consisting of the following: a lab test device, a point-of-care
assay device, a personal electronic device, an electronic medical
record, and a computer system.
[0269] Calculating can be carried out by a Monte Carlo engine and
can be a Bayesian statistical calculation.
[0270] In an embodiment, a plurality of medical conditions is at
least four medical conditions, for example from the group
consisting of: prostatitis due to inflammation, prostatitis due to
infection, prostate cancer, benign prostate hyperplasia, and no
prostate disease. A biomarker value can be from a PSA or fPSA
assay.
[0271] A result of a course of medical action can be selected from
the group consisting of the following: a test result, a diagnosis,
a cure, an effect, and no effect. Posterior probabilities can be
delivered to a user through an electronic medical record or a
webpage or an electronic device with a display or a printout.
[0272] In an embodiment, the computer system comprises a processor,
a storage unit, and a device for network communication.
[0273] In an embodiment, a business method is carried out for a
fee, for example each delivery of posterior probabilities is
carried out for a fee.
[0274] A business method can further comprise suggesting a course
of medical action to said user based on said posterior
probabilities, and the suggestion can be provided for a fee.
[0275] In an embodiment of a business method of the invention, a
posterior probability of a medical condition is delivered to a
user, wherein the user, without limitation, is a patient, a medical
person (such as a physician), a health systems, or a lab. For
example, a subject can have a blood test that is assayed in a lab
or at the point-of-care and then a user sends the information from
the assay to company (such as over the internet), where the company
performs processes or calculations with the information and
delivers an output (such as a probability of the occurrence of a
disease) to the user. The company can provide the output for a
fee.
[0276] In an embodiment, a business method comprises selling
services of the calculations and delivery of information directly
to patients. For example, the patients can use this information
with their physicians.
[0277] In an embodiment, tokens can be sold to a user for a company
to perform a calculation method the invention and delivery of the
results of the calculation method to the user. For example, tokens
can be sold singly or in blocks of more than one. Further, each
token can allow the user to obtain one analysis as delivered by a
business method of the invention.
[0278] In another embodiment, a user of a token is a physician,
insurance company, or health system. In another embodiment of a
method when a subject has a periodically scheduled test, the
results of the tests and other patient information, including a
history of biomarker results, can be entered or uploaded to a
computer system and then the company can analyze the data and
provide a report for use that includes probabilities for one or
more medical conditions, for example a doctor reading the report
with a patient.
[0279] A company can also use a method of the invention to sell
services to testing labs. For example, a token method as described
herein can be used. In an embodiment, labs may offer a package of
services that include blood draws, biomarker analysis and analysis
as provided by the invention.
[0280] In another embodiment, a company performs an analysis for
insurance company reimbursement.
[0281] Digital health services, such as WebMD, may offer a
calculation method of the invention as a value added service in
conjunction with medical information and other analysis
services.
[0282] As new technology is developed to deliver blood test results
at the point of care within a short time, perhaps minutes, in an
embodiment of the invention the device doing the test can
communicate to a computer system wirelessly, through a docking
station or other physical link or by other means, including manual
entry of the results. The computer system can have software and/or
a storage medium that receives the test results and other
information about the patient and for performing a calculation
method of the invention. A computer system can be on remote servers
that can process the new data along with other patient information
already stored in the system. Parallel processing can be used to
analyze the data and create a report quickly, perhaps in minutes. A
report can be transmitted to the computer in the doctor's office
for viewing on screen or for printing and use as hard copy. For
example, the doctor may review the report with the patient and
decide on a course of medical action. For example, the doctor and
patient may decide on ultrasound imaging to measure the volume of
the patient's prostate. The prostate volume measured can be entered
into a computer system for further analysis that can then create a
new report that can be transmitted to the doctor's display for
viewing or printing. For example, the doctor may review the new
results with the patient and decide on a new course of medical
action. For example, the doctor and patient may decide to culture
prostate secretions for infection and start a course of antibiotics
to treat the possible infection.
[0283] In another embodiment with new technology developed to
deliver automated blood tests for a variety of biomarkers at one
time, automated protein profile equipment reports the levels of a
wide variety of proteins and other biomarkers in a sample.
Biomarker values can be automatically uploaded to a computer system
as described herein and can be added to other patient information
already stored in the system. For example, new probabilities can be
calculated for all medical conditions being considered. The doctor
and/or the patient can consider the results and choose appropriate
courses of medical action.
[0284] Individuals can vary in their predisposition for various
conditions. In an embodiment, methods of the invention incorporate
these predispositions or risk factors into the prior probabilities
of each condition for each individual. For example, genetic testing
might show a man has a three times higher than normal risk of
prostate cancer. Family history or race might suggest other men
have a two times higher than normal risk of prostate cancer.
Several risk factors can be combined into an overall risk ratio
that reflects a person increased or reduced risk of a condition
compared to an overall population. Risk factors can include without
limitation: gene profile, family history, race, obesity (BMI),
physical condition, geographic location of home and work over time,
diet and exercise regimen, exposure to environmental factors and
other things.
[0285] An individual's future predisposition to various conditions
can depend on their past incidence of that condition and other
related conditions. In another embodiment, methods of the invention
incorporate these predispositions or risk factors into either the
prior probabilities of each condition for each individual or an
explicit algorithm that may be a Bayes process. For example, a man
with a history of prostatitis caused by infection has an increased
risk of that condition in the future. If a prior probability is
adjusted then algorithms are used to combine the risk factor based
on past history with other risk factors into an overall risk ratio
that reflects a person increased or reduced risk of a condition
compared to an overall population. Alternatively, a different
algorithm can be used to calculate a new posterior probability of a
condition based on the details of the past history of that
condition and related conditions, perhaps using a Bayes
process.
[0286] It is to be understood that the exemplary methods and
systems described herein may be implemented in various forms of
hardware, software, firmware, special purpose processors, or a
combination thereof. Preferably, a calculation method of the
present invention is implemented in software as an application
program tangibly embodied on one or more program storage devices.
The application program may be executed by any machine, device, or
platform comprising suitable architecture. It is to be further
understood that, because some of the systems and methods depicted
in the Figures are preferably implemented in software, the actual
connections between the system components (or the process steps)
may differ depending upon the manner in which the method is
programmed. Given the teachings herein, one of ordinary skill in
the related art will be able to contemplate or practice these and
similar implementations or configurations of the present
invention.
[0287] All the examples disclosed herein are to be considered
non-limiting. As an illustration, it should be understood that for
the processing flows described herein, the steps and the order of
the steps may be altered, modified, removed, and/or augmented and
still achieve the desired outcome.
* * * * *