U.S. patent application number 15/418363 was filed with the patent office on 2017-07-20 for self-improving method of using online communities to predict health-related outcomes.
This patent application is currently assigned to PatientsLikeMe Inc.. The applicant listed for this patent is PatientsLikeMe Inc.. Invention is credited to Benjamin Heywood, James Heywood.
Application Number | 20170206327 15/418363 |
Document ID | / |
Family ID | 40549622 |
Filed Date | 2017-07-20 |
United States Patent
Application |
20170206327 |
Kind Code |
A1 |
Heywood; James ; et
al. |
July 20, 2017 |
SELF-IMPROVING METHOD OF USING ONLINE COMMUNITIES TO PREDICT
HEALTH-RELATED OUTCOMES
Abstract
The invention is directed, in part, to method of using
self-reported health data in online communities to predict
significant health events in life-changing illnesses to improve the
lives of individuals and to improve patient self-management. The
invention provides a method for providing real-time personalized
medical predictions for an individual patient. The method includes:
providing a database containing patient information for a plurality
of other patients including one or more attributes for each patient
in the database; constructing a model of a disease based on disease
progressions for the plurality of patients; receiving a request
from the individual patient, the patient associated with one or
more attributes; and making a real-time prediction for the
individual patient based on the mode and the individual patient's
attributes.
Inventors: |
Heywood; James; (Newton,
MA) ; Heywood; Benjamin; (Cambridge, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
PatientsLikeMe Inc. |
Cambridge |
MA |
US |
|
|
Assignee: |
PatientsLikeMe Inc.
Cambridge
MA
|
Family ID: |
40549622 |
Appl. No.: |
15/418363 |
Filed: |
January 27, 2017 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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12251189 |
Oct 14, 2008 |
9589104 |
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15418363 |
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PCT/US08/79674 |
Oct 12, 2008 |
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12251189 |
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61070067 |
Mar 20, 2008 |
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60998768 |
Oct 12, 2007 |
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60998669 |
Oct 12, 2007 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 19/3456 20130101;
G16H 40/20 20180101; A61B 5/4833 20130101; G06F 19/325 20130101;
G06Q 50/24 20130101; G16H 15/00 20180101; A61B 5/0002 20130101;
G06Q 50/22 20130101; G16H 10/20 20180101; G16H 70/60 20180101; A61B
5/4824 20130101; G16H 50/20 20180101; G16H 20/10 20180101; G16H
50/50 20180101; G16H 50/70 20180101; G16H 10/60 20180101; G16H
40/63 20180101; G16H 20/40 20180101; G06F 19/3481 20130101 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Claims
1-20. (canceled)
21. A computer implemented method for providing medical predictions
for an individual patient in a community of patients, the method
comprising: providing a server coupled, via a network, to a
plurality of computers, each computer having a graphical user
interface and being associated with a particular patient wherein
each computer includes a processor configured with executable
instructions to allow each patient of the community of patients to
input self-reported information relating to one or more medical
condition attributes of the particular patient without being
responsive to a series of pre-programmed questions, the server
being configured to: receive the self-reported patient information
from each of the plurality of computers; store the self-reported
patient information in a database; construct a model of a disease
based on disease progressions wherein a disease progression is
based on a patient's disease and self-reported patient information
stored in the database.
22. The computer implemented method as recited in claim 1, wherein
the server is further configured to: receive a request from an
individual patient from the community of patients; and determine a
real-time prediction concerning the effect of an intervention for
the individual patient based on the model and individual patient's
attributes and analyzing an effect of the intervention by obtaining
a difference between or comparing the outcome of a disease progress
with and without an intervention.
23. The method of claim 1, wherein the one or more attributes
includes at least one selected from the group consisting of: age,
race, ethnicity, gender, height, weight, body mass index (BMI),
body volume index (BVI), genotype, phenotype, severity of the
disease, progression rate of the disease, measures of functional
ability, quality of life, interventions, and remedies.
24. The method of claim 1, wherein the disease includes at least
one selected from the group consisting of: neurological diseases,
Amytrophric Lateral Sclerosis (ALS), Multiple Sclerosis (MS),
Parkinson's Disease, Human Immunodeficiency Virus (HIV), Acquired
Immune Deficiency Syndrome (AIDS), depression, mood disorders,
cancer, blood cancer, fibromyalgia, epilepsy, post traumatic stress
disorder, traumatic brain injury, cardiovascular disease,
osteoporosis, chronic obstructive pulmonary disease, arthritis,
allergies, autoimmune diseases, and lupus.
25. The method of claim 1, wherein the model is based on data for a
subset of the plurality of patients and the server is further
configured to process a request from the patient to modify a
composition of the subset of the plurality of patients.
26. The method of claim 2, wherein the server is further configured
to calculate a confidence interval for the prediction, which
includes selecting a set of reported data points from the plurality
of other patients.
27. The method of claim 6, wherein for each of the reported data
points in the set, the server is configured to: obtain a data set
for a corresponding other patient to the reported data point;
calculate a predicted value with the data set and the model;
calculate an error between the predicted value and the reported
data point; determine a distribution of the errors; and calculate a
confidence interval from the distribution.
28. The method of claim 2, wherein the difference is measured for a
plurality of individual patients.
29. The method of claim 2, wherein the difference is compared to
the distribution of error.
30. The method of claim 2, wherein the difference is compared to
the confidence interval for the model.
31. The method of claim 10, wherein the server is further
configured to identify one or more of the differences that exceed
the confidence interval for the model.
32. The method of claim 6, wherein the confidence interval is
calculated with a chi-square test.
33. The method of claim 6, wherein the confidence interval is
calculated from a measure of variance of the individual patient's
attributes.
34. The method of claim 6, wherein the confidence interval is
calculated by comparing the individual patient's attributes to a
model fit for the individual patient using the model.
35. A non-transitory and tangible computer-readable medium whose
contents cause a computer to perform a computer-implemented method
for providing medical predictions for an individual patient in a
community of patients, the method comprising: receiving, by a
server, self-reported patient information from a plurality of
computers wherein each computer has a graphical user interface
associated with a particular patient with each computer including a
processor configured with executable instructions to allow each
patient of the community of patients to input self-reported
information relating to one or more medical condition attributes of
the particular patient; store, by the server, the self-reported
patient information in a database; construct, by the server, a
model of a disease based on disease progressions wherein a disease
progression is based on a patient's disease and self-reported
patient information stored in the database; receive, by the server,
a request from an individual patient from the community of
patients; and determine, by the server, a real-time prediction
concerning the effect of an intervention for the individual patient
based on the model and individual patient's attributes and
analyzing an effect of the intervention by obtaining a difference
between or comparing the outcome of a disease progress with and
without an intervention.
36. The non-transitory and tangible computer-readable medium as
recited in claim 15, wherein each patient of the community of
patients inputs the self-reported information relating to one or
more medical condition attributes of the particular patient without
being responsive to a series of pre-programmed questions.
37. The non-transitory and tangible computer-readable medium as
recited in claim 15, wherein the model is based on data for a
subset of the plurality of patients and the server is further
configured to process a request from the patient to modify a
composition of the subset of the plurality of patients.
38. The non-transitory and tangible computer-readable medium as
recited in claim 15, wherein the server further calculates a
confidence interval for the prediction, which includes selecting a
set of reported data points from the plurality of other
patients.
39. The non-transitory and tangible computer-readable medium as
recited in claim 15, wherein the difference is measured for a
plurality of individual patients.
40. The non-transitory and tangible computer-readable medium as
recited in claim 15, wherein the difference is compared to the
distribution of error.
Description
RELATED APPLICATION
[0001] This application is a continuation of PCT/US08/79674, filed
on Oct. 12, 2008, and claims priority to U.S. Provisional Patent
Application No. 60/998,669, filed on Oct. 12, 2007, U.S.
Provisional Patent Application No. 60/998,768, filed on Oct. 12,
2007, and U.S. Provisional Patent Application No. 61/070,067, filed
on Mar. 20, 2008. The entire contents of each of these applications
is hereby incorporated by reference herein.
TECHNICAL FIELD
[0002] The present invention is directed to a method of using
self-reported health data in online communities to predict
significant health events in life-changing illnesses to improve the
lives of individuals and to improve patient self-management.
BACKGROUND OF THE INVENTION
[0003] According to the World Health Organization, chronic diseases
are now the major cause of disability and death worldwide,
accounting for 59% of 57 million deaths annually and 46% of the
global burden of disease. According to the U.S. Centers for Disease
Control and Prevention, more than 90 million Americans live with
chronic illnesses, accounting for more than 75% of the national
$1.4 trillion bill for medical care costs. Chronic diseases also
account for one-third of the years of potential life lost before
age 65. Although widespread illnesses such as cardiovascular
disease or diabetes are well-characterized in terms of
risk-factors, prevention, and treatment, there are a host of
under-researched and untreatable conditions; the National
Organization for Rare Disorders (NORD) tracks approximately 6,000
rare disorders which, altogether, affect 23 million Americans.
[0004] Research into the relationship between health behavior and
outcomes suffers from a variety of methodological flaws. There is
often insufficient funding for prospective follow-up studies,
service provision may be dependent on research staff, sample sizes
are small, and particularly in the case of rare diseases,
recruitment is difficult. Furthermore, results from clinical
research tend to be written in scientific jargon, are difficult for
the general public to understand, and refer to group averages
rather than individual outcomes.
[0005] Accordingly, there is a need for an effective process to (i)
collect data on interventions and health outcomes, (ii) model the
likely course of a disease for an individual on the basis of their
background and experience to-date, (iii) provide information on
likely outcomes to the individual to help them manage their
condition, and (iv) improve the model to improve the accuracy of
predictions made.
[0006] The term "intervention" refers any event that has a
positive, negative, or neutral effect on one or more medical
conditions. The term intervention includes a variety of activities
including, but not limited to, administration of a medication,
administration of a remedy, administration of a nutritional
supplement, administration of a vitamin, exercise, physical
therapy, massage, stretching, consumption of food, rest, and
sleep.
SUMMARY OF THE INVENTION
[0007] The present invention meets the foregoing need and provides
an effective method of predicting health outcomes for an individual
with a life-changing health condition, which will result in greater
empowerment over their healthcare and better outcomes apparent from
the discussion herein.
[0008] The invention provides a method for providing real-time
personalized medical predictions for an individual patient. The
method includes: providing a database containing patient
information for a plurality of other patients including one or more
attributes for each patient in the database; constructing a model
of a disease based on disease progressions for the plurality of
patients; receiving a request from the individual patient, the
patient associated with one or more attributes; and making a
real-time prediction for the individual patient based on the mode
and the individual patient's attributes.
[0009] The one or more attributes can include at least one selected
from the group consisting of: age, race, ethnicity, gender, height,
weight, body mass index (BMI), body volume index (BVI), genotype,
phenotype, severity of the disease, progression rate of the
disease, measures of functional ability, quality of life,
interventions, and remedies.
[0010] The disease can include at least one selected from the group
consisting of: neurological diseases, Amyotrophic Lateral Sclerosis
(ALS), Multiple Sclerosis (MS), Parkinson's Disease, Human
Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome
(AIDS), depression, mood disorders, cancer blood cancer,
fibromyalgia, epilepsy, post traumatic stress disorder, traumatic
brain injury, cardiovascular disease, osteoporosis, chronic
obstructive pulmonary disease, arthritis, allergies, autoimmune
diseases, and lupus.
[0011] The data returned can include individual data for one or
more members of the set of other patients. The data returned can
include aggregate data for one or more members of the set of other
patients. The method can include processing a request from the
patient to view individual data.
[0012] The model can be based on data for a subset of the plurality
of patients. The method can include processing a request from the
patient to modify a composition of the subset of the plurality of
patients. The composition of the subset of other patients can
defined by fuzzy logic. The step of modifying the composition of
the subset of the plurality of patients can include modifying the
range of attributes of patients within the subset. The step of
modifying the composition of the subset of the plurality of the
patients can include modifying the importance of attributes of
patients in composing the subset. The method can include conducting
a multivariate pattern matching search of data related to the
plurality of patients.
[0013] The method can include calculating a confidence interval for
the prediction. The step of calculating a confidence interval for
the prediction can include: selecting a set of reported data points
from the plurality of other patients, for each of the reported data
points in the set: obtaining a data set for the corresponding other
patient to the reported data point calculating a predicted value
with the data set and the model, and calculating an error between
the predicted value and the reported data point; producing a
distribution of the errors; and calculating a confidence interval
from the distribution.
[0014] The set of reported data points can include n closest
reported data points to the prediction. The set of reported data
points can include reported data points within an ellipsoid defined
by a distance metric. The size of the data set for the
corresponding other patient can be comparable to a quantity of
attributes associated with the individual patient.
[0015] The method can include analyzing an effect of an
intervention by measuring a difference between a prediction absent
the intervention and a reported outcome with the intervention. The
difference can be measured for a plurality of individual patients.
The difference can be compared to the distribution of error. The
difference can be compared to the confidence interval for the
model.
[0016] The method can include identifying one or more of the
differences that exceed the confidence interval for the model. The
method can include assembling a distribution of the differences for
the plurality of individual patients, and computing a standard
error for the distribution. The confidence interval can be
calculated with a chi-square test. The confidence interval can be
calculated from a measure of variance of the individual patient's
attributes. The confidence interval can be calculated by comparing
the individual patient's attributes to a model fit for the
individual patient using the model.
[0017] The invention also provides a computer-readable medium whose
contents cause a computer to perform a method for providing
real-time personalized medical predictions for an individual
patient. The method includes: providing a database containing
patient information for a plurality of other patients including one
or more attributes for each patient in the database; constructing a
model of a disease based on disease progressions for the plurality
of patients; receiving a request from the individual patient, the
patient associated with one or more attributes; and making a
real-time prediction for the individual patient based on the mode
and the individual patient's attributes.
[0018] The invention also provides a method for providing real-time
personalized medical predictions. The method includes: gathering
patient-submitted information from a community of patients having a
disease, the information including medical condition metrics and
intervention data; utilizing the patient-submitted information to
form a model of the disease; and predicting the progression of the
disease in a particular patient by applying information submitted
by an individual patient to the model.
[0019] The information submitted by the individual patient can
include a date of onset of the disease. The step of predicting the
progression of the disease can be performed in real time. The step
of predicting the progression of the disease can include providing
a confidence interval. The step of predicting the progression of
the disease can include providing a graphical prediction. The
graphical prediction can be a line chart depicting development of
the disease with regard to a rating scale. The disease can include
at least one selected from the group consisting of: neurological
diseases, Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis
(MS), Parkinson's Disease, Human Immunodeficiency Virus (HIV),
Acquired Immune Deficiency Syndrome (AIDS), depression, mood
disorders, cancer blood cancer, fibromyalgia, epilepsy, post
traumatic stress disorder, traumatic brain injury, cardiovascular
disease, osteoporosis, chronic obstructive pulmonary disease,
arthritis, allergies, autoimmune diseases, and lupus. The model can
be a model of the disease's pathology.
[0020] The invention provides a method for providing real-time
personalized medical predictions for an individual patient. The
method includes providing a database containing patient information
for a plurality of patients including one or more attributes for
each patient in the database; providing a graphical user interface
displaying one or more attributes of the individual patient, the
graphical user interface allowing the patient to formulate a search
request specifying at least one of the attributes; searching the
database of patient information for patients having the specified
one or more attributes; providing a model of a disease based on
disease progressions for the patients having the specified one or
more attributes; and making a real-time prediction for the
individual patient based on the model.
[0021] The one or more attributes can include at least one selected
from the group consisting of: age, race, ethnicity, gender, height,
weight, body mass index (BMI), body volume index (BVI), genotype,
phenotype, severity of the disease, progression rate of the
disease, measures of functional ability, quality of life,
interventions, and remedies.
[0022] The disease can include at least one selected from the group
consisting of: neurological diseases, Amyotrophic Lateral Sclerosis
(ALS), Multiple Sclerosis (MS), Parkinson's Disease, Human
Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome
(AIDS), depression, mood disorders, cancer blood cancer,
fibromyalgia, epilepsy, post traumatic stress disorder, traumatic
brain injury, cardiovascular disease, osteoporosis, chronic
obstructive pulmonary disease, arthritis, allergies, autoimmune
diseases, and lupus.
[0023] The data returned can include individual data for one or
more members of the set of other patients or aggregate data for one
or more members of the set of other patients. The method can
include processing a request from the patient to view individual
data. The method can also include processing a request from the
patient to modify a composition of the set of other patients. The
composition of the set of other patients can be defined by fuzzy
logic.
[0024] The step of modifying the composition of the set of other
patients can include modifying the range of attributes of patients
within the set. The step of modifying the composition of the set of
other patients can include modifying the importance of attributes
of patients in composing the set. The method can include conducting
a multivariate pattern matching search of data related to the other
patients.
[0025] The outcome data can include at least one medical condition
selected from the group consisting of: occurrence of epilepsy,
occurrence of migraine, pain, fatigue, cognitive ability, anxiety,
mobility, dexterity, and occurrence of allergies.
[0026] The method can include calculating a confidence interval for
the model. The method can also include analyzing an effect of an
intervention by measuring a difference between an expected outcome
absent the intervention as predicted by the model and a reported
outcome with the intervention. The difference can be measured for a
plurality of individual patients. The difference can be a sum of
the observational error rate based on a quality of the plurality of
patient's pre-model data and a variation from the model. The
difference can be compared to the confidence interval for the
model.
[0027] The invention also provides a computer-readable medium whose
contents cause a computer to perform a method for providing
real-time personalized medical predictions for an individual
patient. The method includes: providing a database containing
patient information for a plurality of patients including one or
more attributes for each patient in the database; providing a
graphical user interface displaying one or more attributes of the
individual patient, the graphical user interface allowing the
patient to formulate a search request specifying at least one of
the attributes; searching the database of patient information for
patients having the specified one or more attributes; providing a
model of a disease based on disease progressions for the patients
having the specified one or more attributes; and making a real-time
prediction for the individual patient based on the model.
[0028] The invention also provides a method for providing real-time
personalized medical predictions. The method includes: gathering
patient-submitted information from a community of patients having a
disease, the information including medical condition metrics and
intervention data; utilizing the patient-submitted information to
form a model of the disease; and predicting the progression of the
disease in a particular patient based on information submitted by
an individual patient.
[0029] The step of predicting the progression of the disease can be
performed in real time. The step of predicting the progression of
the disease can include providing a confidence interval. The step
of predicting the progression of the disease can include providing
a graphical prediction. The graphical prediction can be a line
chart depicting development of the disease with regard to a rating
scale.
[0030] The disease can include at least one selected from the group
consisting of: neurological diseases, Amyotrophic Lateral Sclerosis
(ALS), Multiple Sclerosis (MS), Parkinson's Disease, Human
Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome
(AIDS), depression, mood disorders, cancer blood cancer,
fibromyalgia, epilepsy, post traumatic stress disorder, traumatic
brain injury, cardiovascular disease, osteoporosis, chronic
obstructive pulmonary disease, arthritis, allergies, autoimmune
diseases, and lupus.
[0031] The invention also provides a method for providing
personalized medical information comprising: providing a database
containing patient information for a plurality of patients
including one or more attributes for each patient in the database;
providing a graphical user interface displaying one or more
attributes of a patient, the graphical user interface allowing the
patient to formulate a search request specifying at least one of
the attributes; searching the database of patient information for
patients having the specified one or more attributes; and returning
data to the patient identifying a set of other patients having the
specified one or more attributes.
[0032] The one or more attributes can include at least one selected
from the group consisting of: age, race, ethnicity, gender, height,
weight, body mass index (BMI), body volume index (BVI), genotype,
phenotype, disease, disease severity, disease progression rate,
measures of functional ability, quality of life, interventions, and
remedies.
[0033] The database can include one or more correlations between an
attribute and at least one secondary attribute selected from the
group consisting of: quality of life, functional ability, pain, and
treatment intensity.
[0034] The disease can include at least one selected from the group
consisting of: Amyotrophic Lateral Sclerosis (ALS), Multiple
Sclerosis (MS), Parkinson's Disease, Human Immunodeficiency Virus
(HIV), Acquired Immune Deficiency Syndrome (AIDS), depression, mood
disorders, cancer blood cancer, fibromyalgia, epilepsy, post
traumatic stress disorder, and traumatic brain injury.
[0035] The data returned can include individual data for one or
more members of the set of other patients. The data returned can
include aggregate data for one or more members of the set of other
patients.
[0036] The method can include processing a request from the patient
to view individual data. The method can also include processing a
request from the patient to modify a composition of the set of
other patients. The composition of the set of other patients can be
defined by fuzzy logic. Modifying the composition of the set of
other patients can include modifying the range of attributes of
patients within the set. Modifying the composition of the set of
other patients can include modifying the importance of attributes
of patients in composing the set. The composition of the set of
other patients can be defined by an optimal matching algorithm on a
graph of attribute similarity metrics. The composition of the set
of other patients can be defined by a scalar-vector decomposition
on a matrix of similarities of attributes of the set of other
patients. The method can also include conducting a multivariate
pattern matching search of data related to the other patients.
[0037] The invention also provides a computer-readable medium whose
contents cause a computer to perform a method for providing
personalized medical information. The method includes the steps of:
providing a database containing patient information for a plurality
of patients including one or more attributes for each patient in
the database; providing a graphical user interface displaying one
or more attributes of a patient, the graphical user interface
allowing the patient to formulate a search request specifying at
least one of the attributes; searching the database of patient
information for patients having the specified one or more
attributes; and returning data to the patient identifying a set of
other patients having the specified one or more attributes.
[0038] The invention also provides a method for providing
personalized medical information. The method includes the steps of:
providing a database containing patient information for a plurality
of patients including one or more attributes for each patient in
the database; providing a graphical user interface displaying one
or more attributes of a patient, the graphical user interface
allowing the patient to formulate a search request specifying at
least one of the attributes; searching the database of patient
information for patients having the specified one or more
attributes; and providing outcome data for other patients that
previously had similar attributes to the specified one or more
attributes.
[0039] The one or more attributes can include at least one selected
from the group consisting of: age, race, ethnicity, gender, height,
weight, body mass index (BMI), body volume index (BVI), genotype,
phenotype, disease, disease severity, disease progression rate,
measures of functional ability, quality of life, interventions, and
remedies.
[0040] The disease can include at least one selected from the group
consisting of: Amyotrophic Lateral Sclerosis (ALS), Multiple
Sclerosis (MS), Parkinson's Disease, Human Immunodeficiency Virus
(HIV), Acquired Immune Deficiency Syndrome (AIDS), depression, mood
disorders, cancer, blood cancer, fibromyalgia, epilepsy, post
traumatic stress disorder, and traumatic brain injury.
[0041] The data returned can include individual data for one or
more members of the set of other patients. The data returned can
include aggregate data for one or more members of the set of other
patients.
[0042] The method can also include processing a request from the
patient to view individual data. The method can also include
processing a request from the patient to modify a composition of
the set of other patients. The composition of the set of other
patients can be defined by fuzzy logic. Modifying the composition
of the set of other patients can include modifying the range of
attributes of patients within the set. Modifying the composition of
the set of other patients can also include modifying the importance
of attributes of patients in composing the set.
[0043] The method can also include conducting a multivariate
pattern matching search of data related to the other patients. The
outcome data can include at least one medical condition selected
from the group consisting of: occurrence of epilepsy, occurrence of
migraine, pain, fatigue, cognitive ability, anxiety, mobility,
dexterity, and occurrence of allergies.
[0044] The invention also provides a computer-readable medium whose
contents cause a computer to perform a method for providing
personalized medical information. The method can include: providing
a database containing patient information for a plurality of
patients including one or more attributes for each patient in the
database; providing a graphical user interface displaying one or
more attributes of a patient, the graphical user interface allowing
the patient to formulate a search request specifying at least one
of the attributes; searching the database of patient information
for patients having the specified one or more attributes; and
providing outcome data for other patients that previously had
similar attributes to the specified one or more attributes.
[0045] Accordingly, a method of modelling an individual's disease
progression using an online community can include the steps of
creating an online community for people with life-changing
illnesses; creating membership accounts for patients joining the
online community; receiving personal and medical information from
the patient; categorizing and storing the received personal and
medical information to a member database; modelling the
relationship between an individual's background and current health
status in comparison to data received from other patients like
them; making predictions about future outcomes which are presented
to the patient with levels of confidence; allowing predictions to
be modified on the basis of received information from health
providers or administrative records systems, such as utilization of
health services, laboratory test results, diagnostic procedures,
therapeutic procedures, or from other measurement systems of
phenotypic or genotypic characteristics; and receiving feedback
from members over the course of time to validate or modify the
model accordingly in order to improve the model.
[0046] The present invention also provides a method of displaying
visual information to a patient about their likely predicted
disease states, health events, and health outcomes which includes
estimations of confidence surrounding the likelihood of such
outcomes, which can change over time in response to more data from
themselves, from other sources such as health providers or
administrative systems, or other members of the site.
[0047] The invention also provides a method of grouping patients by
background and illness-specific characteristics including but not
limited to: the presence or absence of a genetic mutation; the
presence or absence of a genetic polymorphism; the presence of
absence of a pattern of familial inheritance apparent from a family
history; the presence or absence of a known or unknown proteome
sequence; the staging of disease progression according to
self-report or assessment by a healthcare professional; functional
outcome assessed by a self-report questionnaire or by a healthcare
professional; demographic information submitted by the patient
including, but not restricted to age, sex, ethnicity,
socio-economic status, health behaviours, diet, exercise, smoking
history, drug use, surgical history, past and present geographical
location, personality, and/or chance; and the rate, nature,
direction of change, or interaction of any of the above. The method
of grouping can include but is not limited to multiple regression
to identify significant predictor variables such as suggested
above.
[0048] The present invention also provides a method by which the
grouping of patients permits the construction of a mathematical
model establishing the likelihood of important medical outcomes
such as, without limitation, when a patient may need a health
intervention (e.g., surgery, tablet, therapy, assistive technology,
home modification, nutritional supplement, lifestyle change and the
like); the likelihood of adverse events in response to intervention
(e.g., side effects, injuries, death and the like); the likelihood
that the patient will develop a symptom, condition, or disease; the
likelihood that a child or other relative of the patient will
develop a symptom, condition, or disease; the likelihood that a
patient will need to receive care in an institution as opposed to
receiving care at home; the point at which the costs of paying for
medical care will achieve a certain level; and the degree of
improvement which might be experienced if the patient chooses to
start a given intervention.
[0049] The present invention also includes a method by which a
predicted event is displayed on the patient's medical profile at a
future data, such as the events as noted above. The predicted event
may also be presented with a level of confidence dependent on the
quantity and quality of data entered by the user or by data
available from other sources and users of the system.
[0050] The present invention also includes a method by which models
used to predict future events are strengthened or weakened by
ongoing feedback from users once the predicted event has come to
pass.
[0051] The present invention also provides a server for
facilitating a Web site portal that collects and analyzes
information related to patients having at least one common
characteristic such as a disorder, wherein the server communicates
with clients via a distributed computing network and the patients
and related caregivers can access the Web site portal via a client,
and wherein the server comprises: (a) a memory storing an
instruction set and historical data related to a plurality of
patients; and (b) a processor for running the instruction set, the
processor being in communication with the memory and the
distributed computing network, wherein the processor is operative
to: (i) receive additional data related to the patients and add the
additional data to the historical data; (ii) model the historical
data generally for a subset of the patients with a second common
characteristic; (iii) model the historical data for an individual
patient within the subset of the patients; (iv) make a prediction
of a timeframe for a future event based on the modelled subset and
individual patient historical data; (v) add the prediction to the
historical data; (vi) analyze the prediction based on the
historical data to determine a confidence parameter; and (vii)
provide a display of the historical data modelled for the subset,
the historical data modelled for the individual patient, the
prediction for the individual patient, and the confidence
parameter.
[0052] It should be appreciated that the present invention can be
implemented and utilized in numerous ways, including without
limitation as a process, an apparatus, a system, a device, a method
for applications now known and later developed or a computer
readable medium. These and other unique features of the system
disclosed herein are readily apparent from the subject description
and the accompanying disclosure and drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0053] For a fuller understanding of the nature and desired objects
of the present invention, reference is made to the following
detailed description taken in conjunction with the accompanying
drawing figures wherein:
[0054] FIG. 1 is a diagram depicting an exemplary graphical
element.
[0055] FIG. 2 is a diagram depicting the mapping of the ALSFRS
scale to specific spinal nerves.
[0056] FIG. 3 is a diagram depicting an exemplary user interface
for viewing and refining a group of similar patients and prediction
of disease progression.
[0057] FIG. 4 is a diagram depicting a method of producing a
confidence interval for a predictive model.
[0058] FIG. 5 is a diagram depicting an exemplary graphical element
displaying a prediction and a confidence interval.
[0059] FIG. 6 is a diagram depicting the construction of a
pathological model of ALS from the ALSFRS questionnaire, the
progression of the pathological model, and the use of the
pathological model to predict answers on a future ALSFRS
questionnaire.
DEFINITIONS
[0060] The instant invention is most clearly understood with
reference to the following definitions:
[0061] As used in the specification and claims, the singular form
"a," "an," and "the" include plural references unless the context
clearly dictates otherwise.
[0062] The term "disease" refers to an abnormal condition of an
organism that impairs bodily functions. The term disease includes a
variety of physical ailments including, but not limited to,
neurological diseases (e.g., Amyotrophic Lateral Sclerosis (ALS),
Multiple Sclerosis (MS), Parkinson's Disease), Human
Immunodeficiency Virus (HIV), Acquired Immune Deficiency Syndrome
(AIDS), cancers (e.g., bladder cancer, blood cancer, breast cancer,
colorectal cancer, endometrial cancer, leukemia, lung cancer,
lymphoma, ovarian cancer, pancreatic cancer, prostate cancer, and
skin cancer), diabetes, digestive disorders (e.g., irritable bower
syndrome, gastro esophageal reflux disease, and Crohn's Disease),
cardiovascular diseases, osteoporosis, chronic obstructive
pulmonary disease (COPD), arthritis, allergies, geriatric diseases,
and autoimmune diseases (e.g., lupus). The term disease also
include mental ailments including, but not limited to, depression,
anxiety disorders, post traumatic stress disorder, mood disorders,
psychotic disorders, personality disorders, and eating
disorders.
[0063] The term "medical condition" refers to a manifestation of a
disease such as a symptom. For example, if a patient suffers from
Amyotrophic Lateral Sclerosis (ALS), the patient may experience one
or more medical conditions such as dysphagia (impaired
swallowing).
DETAILED DESCRIPTION OF THE INVENTION
[0064] The invention is directed, in part, to method of using
self-reported health data in online communities to predict
significant health events in life-changing illnesses to improve the
lives of individuals and to improve patient self-management.
Data Acquisition
[0065] Self-reported health data can be gathered from a number of
sources such as the PatientsLikeMe.TM. service available at
www.patientslikeme.com.
[0066] An online community can be created to allow patients to
contribute information about themselves, their diseases, their
medical conditions, and their interventions. Each patient can
register for one or more communities focused on a particular
disease. For example, a patient can join an ALS community. As part
the registration process, the patient can enter various demographic
and/or medical information. Exemplary information can include: age,
race, ethnicity, gender, height, weight, body mass index (BMI),
body volume index (BVI), genotype, phenotype, disease, disease
severity, disease progression rate, measures of functional ability,
quality of life, interventions, remedies, and medical data such as
tests. Patient information can also include historical or
environmental data such as weather data for the patient's
environment (e.g., temperature, humidity, pollen count, air
quality) and the patient's past exposure to the sun. Patient
information can also include personality information. Personality
information can be represented by varius classification systems
such as the DISC assessment, Enneagram of Personality, Keirsey
Temperment Theory, and the Meyer-Briggs Type Indicator. Genotype
can be determined through known SNP (single nucleotide
polymorphism) or full-genome sequencing techniques.
[0067] After registration, the patient periodically inputs
information about one or more medical conditions and one or more
remedies. For example, an ALS patient can indicate when she sleeps,
eats, and takes various medications such as riluzole. Likewise, the
ALS patient can enter data on their functional ability at various
times throughout the day. Rating scales for assessing ALS patients
include the Appel ALS rating scale and the ALS Functional Rating
Scale.
[0068] Referring to FIG. 1, user interface 100 includes a medical
condition metric portion 102, which allows the patient to input a
medical condition metric (in this example, the patient's functional
ability). The user can place multiple data points 104 in the
medical condition metric chart, which includes a time scale. Data
points 104 can be adjusted with respect to time and/or magnitude.
For example, if the patient is indicating how she feels now or at a
designated time, the patient can be limited to moving data point
104 up or down. Alternatively, the patient can input data for a
time by dragging the data point to the left or right. The patient
can be restricted in some embodiments from setting a data point in
the future.
[0069] User interface 100 also includes an intervention portion
108. Intervention portion 108 allows the patient to record one or
more interventions such as administration of a medication,
administration of a remedy, administration of a nutritional
supplement, administration of a vitamin, exercise, physical
therapy, massage, stretching, consumption of food, rest, and sleep.
For example, the patient can designate when meals are eaten by
adjusting bars 110a, 110b, and 110c to indicate the beginning and
ending of the meal. Likewise, the patient can indicate when one or
more drugs 114a-114e are administered by placing markers 112 (which
may depict pills) on a time scale.
[0070] Various types of remedies can be scheduled for specific
times. For example, the patient can be prescribed to take madopar
at 6 A.M. In this situation, user interface 100 can display a
medication schedule. The patient can modify this schedule to
reflect the actually administration by dragging marker 112a.
Likewise, the patient can indicate that the drug was consumed by
clicking on the marker 112a. Clicking on the marker can change the
appearance of the marker 112a (e.g., its color) and thus can be
used by patients, particularly patients with memory problems, to
more faithfully follow a medication program.
[0071] User interface 100 can also include pharmokinetic data, such
a pharmokinetic curve 116 that depicts the concentration of a
medication within the patient over time. Multiple pharmokinetic
curves 116 can be depicted in various colors or patterns to reflect
varying pharmokinetic properties of various medications.
[0072] This patient information is then stored in various formats.
The data can be stored in a relational database. Suitable
relational databases includes DB2.RTM. and INFORMIX.RTM. both
available from IBM Corp. of Armonk, N.Y.; MICROSOFT JET.RTM. and
MICROSOFT SQL SERVER.RTM. both available from the Microsoft Corp.
of Redmond, Wash.; MYSQL.RTM. available from the MySQL Ltd. Co. of
Stockholm, Sweden; ORACLE.RTM. Database, available from Oracle
Int'l Corp of Redwood City, Calif.; and SYBASE.RTM. available from
Sybase, Inc. of Dublin, Calif. Additionally or alternatively, the
data can be stored in a state model.
A Priori Disease Modelling Algorithms
[0073] The invention also provides an a priori method for modelling
diseases. This method can include an a priori model of a
progressive disease. This method does not rely on an underlying
description of a pathology, and thus may be used even for diseases
for which a pathology is unknown.
[0074] The model allows for the prediction of the state of a
disease at some point in time (e.g., past, present, and future)
based on data provided by an individual patient. The data can be
any of the items discussed herein, including, but not limited to
medical condition metrics (e.g. rating scales) and the date of
onset of the disease. The model is designed to provide an accurate
and reliable prediction even where the data provided by the
individual patient and the community of patients is heterogeneous
(e.g., reported at varying time intervals).
[0075] This model describes the state of a disease for a patient at
any point in time as a scalar-valued or vector-valued function. For
example, in a model of ALS, the state can be an integer value in
the closed interval between 0 and 48 (the range of the overall
Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised
(ALSFRS-R) scale) or the state can be a twelve-dimensional vector
indicating the responses to all twelve ALSFRS-R questions. The
ALSFRS-R is a 48-point scale including an questions assessing the
patient ability to walk, breath, communicate, etc.
[0076] The progression of a disease can be described by the time
required since some event (e.g. onset or diagnosis of the disease)
to reach some value in the above-described disease state function.
It may also be described as a differential equation or as the time
required to cross some boundary in the state space. This
progression can be held constant for any particular patient (and
thus be treated as an inherent attribute of the patient's disease)
or it can be variable over the course of the disease.
[0077] Using the progression rate as one dimension, a multivariate
function may describe set of all patients in a population. The
population can include a group of patients, for example, all
patients participating a community such as those provided through
the PatientsLikeMe.TM. service. Each patient is associated with a
set of data points describing their disease state at different
points in time. In the above example of a scalar-valued disease
state function (overall ALSFRS-R score), the function would exist
as a surface (scalar function) of two dimensions: (i) progression
rate and (ii) time since some event (e.g. onset of the disease). In
the example of a vector-valued disease state function, the function
would be a twelve-dimensional vector field as a function of two
dimensions (f(R.sup.2).fwdarw.R.sup.12).
[0078] An expression of this model function can be determined using
numeric methods such as the Levenberg-Marquardt algorithm to
provide an optimal fit according to some appropriate choice of
function. The Levenberg-Marquardt algorithm is described in
publications such as Donald Marquardt, An Algorithm for
Least-Squares Estimation of Nonlinear Parameters, 11 SIAM Journal
on Applied Mathematics 431-41 (1963). For example, a scalar
bi-cubic function of time and progression rate can provide a good
functional form to describe a change in overall ALSFRS-R score over
time.
[0079] The fitness of the model may be improved through an
iterative procedure whereby several first-generation parameters are
replaced with second-generation variants. For example, the initial
value of progression rate can be replaced for each patient in the
data set with a value that provides a least-mean-squares fit to the
model. Thus, the initial approximation of progression rate is
improved by iteration. The model function can then be re-fit to the
new set of parameters and this process can be repeated one or more
times, until some stopping conditions (e.g. convergence or lack of
further change in the progression rates) are met.
[0080] A set of error bounds, or a confidence interval, can be
determined from the actual error rate of the model. To determine
the error bounds around a particular prediction (i.e., a prediction
that a specific patient will have a certain disease state at a
specified time), the method can consider all known patient-reported
data points within a certain radius of the prediction, or
alternatively, the n nearest patient-reported data points for some
positive integer n. In either case, a valid definition of radius
(distance metric) can be derived from the patient's progression
rate or the derivative of the model at the prediction, relating
change in one dimension (time) to change in another dimension (e.g.
overall FRS score). This rate of change defines the shape of an
ellipsoid, and thus a distance metric, around the prediction.
[0081] Consequently, the set of nearby patient-reported data points
may be used to compute a distribution of errors. For each
patient-reported data point in this set, the corresponding patient
is identified and fit into the model as if they only had as many
data points as the patient being predicted (that is, the patient
around whose prediction the confidence interval is being
evaluated). The disease state at the time corresponding to the
patient-reported data point is then predicted from this patient,
and the error (difference between the prediction and the actual,
uncensored data point) is evaluated. This set of errors produces a
distribution, and a desired confidence interval (e.g., a 95%
confidence interval) can be computed by evaluating the distribution
(e.g., the mean error .+-.1.96 .sigma. (standard deviations)). This
confidence interval about a particular prediction can be
transformed into an overall set of confidence bands around the
model for a particular patient by evaluating the error distribution
about a set of time-values. The confidence interval can be smoothed
by fitting the confidence values to some linear or nonlinear
function of time, producing confidence bands that are less
sensitive to areas of the model space where there are fewer known
data points.
[0082] This method is further explained with reference to FIG. 4.
In step 402, a predicted value is calculated for a specific patient
using the models described herein In step 404, the n closest data
points reported by other patients are then retrieved. For each of
these data points (step 406), a data set is obtained in step 408
from the relevant patient (i.e. the patient who previously reported
the data point selected in step 406). This data set can be limited
to the number of data points available for the specific patient.
For example, if the specific patient has entered 50 data points,
only 50 data points will be retrieved for each of the other
patients, regardless of how many data points may be available. The
use of a comparably sized data set permits an accurate estimation
of error based on similar conditions. Each data set is used to
calculate a predicted value with the model (step 410). In step 412,
the error between the predicted value and the actual
patient-reported value is calculated. In step 414, a distribution
of error is assembled. This distribution of error is then used in
step 416 to calculate a confidence interval.
[0083] This confidence interval can be represented graphically as
depicted by shading 502 in FIG. 5 and may also be used to evaluate
the significance of disease interventions.
[0084] In addition, the efficacy of a specific treatment can be
evaluated based upon examining the disease state (e.g., the
ALSFRS-R score) for a set of patients receiving that treatment and
comparing the disease state to the expected model state in the
absence of such treatment. Efficacy of the treatment may be
expressed as a change in the disease state metric responses (e.g.,
"mean of five point greater ALSFRS-R score than expected at six
months") or as a change in the progression rate (e.g., "median
twenty percent less loss of function at four months").
Pathological Disease Modelling Algorithms
[0085] The invention also provides a pathological modeling
algorithms for a disease, which relies on an understanding of the
underlying pathology of the disease, and can be used to make
predictions about a subset of a population or about an
individual.
[0086] This model describes the state of the disease for some
patient at any point in time as a simplified pathological model.
For example, in ALS, a finite array representing motor neurons
along the spinal cord and degrees of neuron death can serve as the
pathological model, as depicted in FIG. 6.
[0087] For Traumatic Brain Injury (TBI), a connected graph
representing regions of the brain and damaged connections (weights
on the edges of the graph) or damaged regions (values of the nodes
of the graph) can serve as the pathological model.
[0088] This model relies on a bidirectional mapping between some
user-provided data set and the pathological model. For example, as
depicted in FIG. 2, individual questions in the ALSFRS-R or
ALSFRS-EX (generically, ALSFRS) can be mapped to individual
vertebra regions, and thus a description of the specific damage to
the motor neurons can be derived from a set of ALSFRS responses.
Similarly, a modeled state of the motor neuron damage may be mapped
back to a set of ALSFRS responses.
[0089] Similarly, in TBI, individual questions in the Craig
Handicap Assessment and Reporting Technique (CHART), the Disability
Rating Scale (DRS), the Level of Cognitive Functioning Scale
(LCFS), and other published surveys can be used individually or in
combination and may be mapped to specific regions of brain trauma
or damage.
[0090] The mapping can be achieved by establishing an association
between individual components (neurons, brain regions, cell types,
protein pathways, organs, or other physiological components,
depending on the disease) to observable functional or diagnostic
effects as communicated by a patient survey, a caregiver survey,
laboratory test results as communicated by the patient or a
caregiver, or other data provided by the patient or a caregiver.
The associations can be established based on existing published or
as-yet-unpublished literature, laboratory experimentation, or
existing physiological models.
[0091] The progression or treatment of the disease can be modeled
by establishing factors for the change of the pathological model.
For example, in ALS, a set of ALSFRS responses may map to the motor
neurons along a certain set of vertebrae being fifty percent
functional. Another set of ALSFRS responses may map to those same
motor neurons being ten percent functional, and another set being
forty percent functional. Based upon a set of these associations,
derived from a set of patients used as a training set, data about
such population being obtained via a set of surveys on a web site,
entry via handheld devices, or other means, a dynamic model can be
developed. For example, as, depicted in FIG. 6, in ALS, a Markov
model or Hidden Markov model can be trained using the Baum-Welch
algorithm to determine an optimal transition matrix describing the
decay of neurons in the pathological model. The Baum-Welch
algorithm is described in publication such as Leonard E. Baum et
al., A Maximization Technique Occurring in the Statistical Analysis
of Probabilistic Functions of Markov Chains, 41(1) Ann. Math
Statist. 164-71 (1970). Additionally or alternatively, a genetic
algorithm can be invoked to determine optimal rates of decay of
neurons in the pathological model in ALS, or to determine rates of
functional adaptation due to retraining of regions of the brain in
TBI.
[0092] As discussed above, and in the context of FIG. 4, a
confidence interval can be determined from the actual error rates
of the model. To determine the error bounds around a particular
prediction, all known patient outcomes (and thus, associated
pathological states) within a certain radius of the prediction can
be considered, or alternatively, the n nearest patient-reported
outcomes for some positive integer n. In either case, a valid
definition of radius (distance metric) can be derived from the
change weights or transition probabilities of the model.
[0093] Consequently, the set of nearby patient-reported outcomes
can be used to compute a distribution of errors. For each
patient-reported data point in this set, the corresponding patient
is identified and fit into the model as if they only had as many
data points as the patient being predicted (i.e., the patient
around whose prediction the confidence interval is being
evaluated). The disease state at the time corresponding to the
patient-reported data point is then predicted from this censored
patient, and the error (difference between the prediction and the
actual, uncensored data point) is evaluated. This set of errors
produces a distribution, and a desired confidence interval (e.g., a
95% confidence interval) can be computed by evaluating the
distribution (e.g., the mean error +/-1.96 standard deviations).
This confidence interval about a particular prediction can be
transformed into an overall set of confidence bands around the
model for a particular patient by evaluating the error distribution
about a set of time-values. The confidence interval can be smoothed
by fitting the confidence values to some linear or nonlinear
function of time, producing confidence bands that are less
sensitive to areas of the model space where there are fewer known
data points.
[0094] This confidence interval can be represented graphically as
depicted in FIG. 5 and can also be used to evaluate the
significance of disease interventions.
[0095] Such a model of disease progression, coupled with such a
bidirectional mapping to functional responses or descriptions of
the disease, can be used to make predictions as to not only the
general course of the disease, but also as to the expected need for
(or probability of need for) particular interventions, or the
probability of a patient experiencing specific symptoms or
outcomes. For example, in ALS, the probability that a patient will
need a wheelchair, or a vent, or a feeding tube, can be expressed
over time (see FIG. 3 herein). Alternatively, the point in time at
which a patient will most likely need an intervention such as one
of these, subject to some confidence interval, can be
predicted.
[0096] In addition, the efficacy of a specific treatment can be
evaluated based upon inferring the state of the pathological model
(based upon observing the functional or diagnostic responses) for a
set of patients receiving that treatment and comparing the model
state to the expected model state in the absence of such treatment.
Efficacy of the treatment can be expressed either as a change in
the pathological model (e.g., "twenty percent reduction in the rate
of neuron degradation over a period of four months") or as a change
in the resulting functional, symptomatic, or diagnostic responses
(e.g., "thirty percent extension in the median time to requiring
the use of a wheelchair").
Data Correction
[0097] Another example of this iterative improvement to the model
is to correct for the time offset for each patient. Depending on
the disease and the specifics of the model, patients may be
reporting their date of onset incorrectly due to improper
recollection, the imprecise definition of `first symptom`, or the
fact that some disease variants may have more obvious first
symptoms than others. A correction offset to the onset date for
each patient (effectively adding Q to the time value of each data
point, where Q is some real-valued number) may reduce the
least-mean-squares error of the model. Given that the patient's
corrected onset date must be before the date of diagnosis, the
optimization for Q may be performed on some bounded interval, e.g.,
Q={q.epsilon.R: q.gtoreq.d, q.ltoreq.d} where d is the time
interval between stated onset and diagnosis. This optimization may
be performed using a numerical method such as Brent's method.
Brent's method is discussed in publications such as R. P. Brent,
Algorithms for Minimization without Derivatives (1973). The model
function can be re-fit to the new set of parameters in this
instance as well, and this process can be repeated one or more
times, until some stopping conditions (e.g. convergence, or lack of
further change in the value of Q) are met.
[0098] Additionally, missing data algorithm (including regression
or expectation maximization (EM) algorithms) can be used to
generate complex datasets for fitting. These datasets include flags
for imputed values to allow sensitivity analyses that estimate the
impact of missing value imputation on model-based forecasts and
predictions.
Model Improvement
[0099] Exploratory data analysis techniques can be utilized to
explore which variables and combinations of variables are
associated with the response (outcome, event, disease state) of
interest. A sample, or training dataset is analyzed using simple
multiple regression methods (in small, well-behaved samples) or
exploratory methods for very large datasets, with or without
missing values. In one example, the CHAID (CHi-squared Automatic
Interaction Detector) method, involves analyzing and ordering every
possible combination of attributes. The analyst guides the analysis
via parameter settings in the CHAID algorithms to identify which
attribute is the most important predictor, which attributes have no
predictive value, and how attributes combine or interact together
to help in prediction of the response.
[0100] In cases where groups of patients are expected to have
different response processes (i.e., where different variables are
influencing and determining responses) cluster analysis methods may
be used to optimize group identification and assignment. Both
agglomerative (bottom-up) and divisive (top-down) clustering
algorithms, with control over distance measures (which guide the
measurement of similarity of the cases being considered for group
assignment), can be used.
[0101] Linear statistical models, such as logit, probit, and
proportional hazards can be utilized to produce forecasts and
estimated responses.
[0102] Model fit can be improved through the use of Neural
Networks, or non-linear statistical models of response. These
models are appropriate for pattern recognition and modeling when
clusters of processes and sub-processes must be taken into account
to optimize forecasts and estimates of response.
Prediction of Disease Progression:
[0103] The disease modelling algorithms herein can be used to
provide personalized predictions of a particular patient's
experience. For example, upon diagnosis with a disease, the patient
can enter information about the themselves, the disease, and one or
more medical conditions. This information is then fed into the
algorithms to determine where the patient is along the progression
of a disease. For example, as depicted in FIG. 6, the algorithms
can predict what an ALS patient's ALSFRS-R value will be at a give
point in the future. Likewise, the algorithms can predict when
certain events will occur, such as confinement to wheel chair or
use of a ventilator. Such predictions can be presented to the
patient with estimations of confidence in the prediction.
[0104] Referring to FIG. 3, an exemplary user interface 300 is
provided for viewing and refining a prediction of disease
progression. An icon 302 represents the patient controlling the
system. The icon 302 includes several color-coded boxes 304, which
represent the status of various body systems or regions (e.g., the
legs, the spine, and the eyes).
[0105] The user interface 300 also includes a population chooser
interface 306 for refining the prediction by expanding or
contracting the population on which the prediction is made. For
example, a patient can initially view a prediction based on all
patients within a community (e.g. all patients with ALS). The
patient can then alter one or more parameters such as age, gender,
race, ethnicity, genotype, etc. The predictions can be updated in
real time as the population is altered. In the depicted example,
the user can alter the population by sliding one or more sliders
308 to adjust the relative importance a factor such as profile
(e.g., age, gender, race, ethnicity, socioeconomic status), genome,
disease, function (e.g., as assessed by the ALSFRS-R scale),
interventions (e.g., medications consumed), and symptoms (e.g.,
dysphagia).
[0106] The user interface 300 can display icons 310a-310f for one
or more patients that are similar to the patient. Icons 310 can be
updated as the patient alters the population using population
chooser interface 306. The patient can "drill down" to view
specific details and profiles of one or more patients, for example,
by clicking on one of the icons 310.
[0107] The user interface 300 can also include one or more charts
312, 314 depicting predictions of the progression of the patient's
disease. Chart 312 depicts the probability of the patient either
(i) recovering from the disease, (ii) living with the disease, or
(iii) dying over a twenty-five year period. Chart 314 predicts the
probability of the patient requiring assistive devices such as a
feeding tube, a wheelchair, or a ventilator over the next
twenty-five years.
[0108] The user interface can include a graphical element (not
shown) that depicts the reliability of the prediction. For example,
the graphical element can be modelled after traffic light. A red
light can indicate that the prediction lacks a certain level of
statistical significance. A yellow light can indicate that the
prediction has an intermediate level of statistical significance. A
green light can indicate that the prediction has an acceptable
level of statistical significance.
[0109] The invention can also compute the effect of various
stochastic and probabilistic events. For example, the invention can
display two different predictions. The first prediction displays
the progression of the patient's disease if the patient develops
pneumonia; the second prediction displays the progression of the
patient's disease if the patient does not develop pneumonia. The
invention can also display advice on preventing pneumonia.
[0110] The invention can also incorporate the probability of such
events into the predictive model. This can be accomplished, e.g.,
through the use of swarm or multiple agent simulation based on
known state transition probabilities, as expressed in Markov
chains. Sample measurements can then be taken at arbitrary points
in time to determine probabilities of outcomes based on certain
criteria. Such criteria can be controllable (e.g., receiving a
certain intervention) or uncontrollable (e.g., developing
pneumonia).
[0111] Additionally, the invention can simulate the effect of
earlier actions that were either taken or not taken. For example, a
patient can display the predicted disease progression for colon
cancer if the cancer was detected two years earlier. Such a
simulation can have a powerful effect on the patient's friends and
family.
Verification of Predictions
[0112] In order to further refine the predictions, the methods
described herein can include ways of verifying the accuracy of
prior predictions. For example, if the algorithm predicts that a
patient will be confined to a wheel chair by Feb. 1, 2009, the
algorithm can send an email to the patient on or about this date to
determine whether this prediction was accurate. Additionally or
alternatively, the patient can continue to provide updated data to
the algorithm that minimizes the need for follow up emails.
[0113] This newly acquired data is added to the population data and
is reflected in further revisions of the predictive models.
Detection of Disease Subgroups
[0114] The invention also enables the detection of rare disease
subgroups. For example, certain genotypes exhibit increased or
decreased resistance to various diseases. Additional genotypes can
be identified by detecting a group of patients that deviate
substantially from the predicted disease progression and analyzing
the genotypes and other data related to the patients.
Identification of New Interventions and Off-Label Uses of
Medications
[0115] The invention also enables the identification of new
interventions and off-label uses of medications. Such interventions
and off-label uses can be effected by analyzing data for a
population having a disease, identifying patients who experience an
improvements in disease progression or symptom severity as a result
of an intervention, and identifying the intervention. Given the
potentially large size of patient communities, the invention is of
particular value to pharmaceutical researchers looking to identify
off-label uses of existing medications.
Software/Hardware Implementations
[0116] A web-based data-processing system can be used to implement
the invention described herein. Web-based data-processing systems
are well known in the art and can include a client computer and a
server computer. The client and server computers can be coupled to
each other over the Internet. Alternatively, the client and server
computers can be coupled to each other over an intranet, for
example, behind a firewall of a private corporate network. The
private corporate network can be the network for a private
hospital.
[0117] The client computer can include a client software program
for executing software applications. The client software program
can be an Internet browser such as INTERNET EXPLORER.RTM.,
available from Microsoft Corporation of Redmond, Wash.,
FIREFOX.RTM., available from the Mozilla Foundation of Mountain
View, Calif., or OPERA.RTM., available from Opera Software AS of
Oslo, Norway. The Internet browser can display content encoded in a
variety of standards such as Hyper Text Markup Language (HTML), and
FLASH.RTM., AIR.RTM., and ACROBAT.RTM. platforms available from
Adobe Systems of San Jose, Calif. User interfaces can include
standard web input elements such as text boxes and toggle buttons
for entering text and selecting options. The client computer can
include input devices, such as a mouse, keyboard, or touch screen
for entering information into the user interface.
[0118] The client computer need not be a personal computer per se,
but rather encompasses devices such as handheld devices, personal
digital assistants, and cellular phones. Mobile devices
advantageously allow for more frequent data collection as well as
well as reminders for patients to engage in an interventions such
as consumption of medication. Suitable mobile device can be
specifically constructed for the methods described herein or can be
existing mobile devices such a smart phones available under the
BLACKBERRY.RTM. trademark from Research in Motion Limited of
Waterloo, Ontario, the PALM.RTM. trademark from Palm, Inc. of
Sunnyvale, Calif., and the IPHONE.TM. trademark from Apple, Inc. of
Cupertino, Calif.
[0119] The user interface can also be a text-based interface. For
example, the server can send a text message or an email to a
cellular phone or a smart phone asking how the patient is feeling.
The patient can respond with an appropriate answer.
[0120] Likewise, the user interface can be an audio interface in
which the server periodically places a telephone call to the
patient asking how the patient is feeling. The patient can respond
verbally, which will be then processed according to known voice
recognition software.
[0121] The server computer can include a server software program
including a web server, for example, Apache Server, and an
application server, for example, Cold Fusion Application Server.
The server computer can include a database server or engine for
encoding and storing data. Suitable database software includes
include DB2.RTM. and INFORMIX.RTM., both available from IBM Corp.
of Armonk, N.Y.; MICROSOFT JET.RTM. and MICROSOFT SQL SERVER.RTM.,
both available from the Microsoft Corp. of Redmond, Wash.;
MYSQL.RTM., available from the MySQL Ltd. Co. of Stockholm, Sweden;
ORACLE.RTM. Database, available from Oracle Int'l Corp of Redwood
City, Calif.; and SYBASE.RTM., available from Sybase, Inc. of
Dublin, Calif.
[0122] The client software program can be used to provide a user
interface for entering personalized data related to a patient, for
example, a patient diagnosed with ALS. The personalized data can
include patient name, sex, and age. The personalized data can
include a medical condition metric, for example, whether a patient
is feeling great, good, fair, poor, or awful. The personalized data
can be submitted to the server software program and the server
software program can receive the personalized data.
[0123] The server program can store the personalized data in memory
on the server computer. The memory can be used to store a data
structure including entries for the personalized data. The data
structure can be a structured data file or a relational
database.
[0124] The server software program can analyze the data, for
example, using function calls executing on a microprocessor. The
server software program can generate a graphical element for
representing the personalized data and send the graphical element
to the client software program. The graphical element can be sent
over the Internet 162 and received by the client software program.
The client software program can display the graphical element.
[0125] The graphical element can be generated and sent as an image
or as a series of values for constructing the graphical element.
The image can be sent to the client software program, which can
display the image. Alternatively, a series of values can be sent to
the client software program, which the client software program can
use to construct and display the graphical element. For example, a
plug-in executing in an Internet browser can be used to construct
and display the graphical element. The plug-in can include special
controls for interacting with the graphical element, including
sliders for moving medical condition metrics.
[0126] The server software program can also store, analyze,
generate, and send to the client software program medical outcome
correlations for relating aspects of the medical condition, as
further explained herein.
Administrative Tools
[0127] The invention includes an administrative tool for use by the
scientific and medical staff to evaluate the models used to assess
the progression and/or severity of disease. Using statistical
techniques, the utility of every predictor variable in the database
can be assessed by ranking the predictors by their R.sup.2
values.
EXAMPLES
Example 1: Wheelchairs in ALS
[0128] A 55-year old male diagnosed with an inherited form of
amyotrophic lateral sclerosis enters data stating that he has been
tested for a mutation of the super-oxide-dismutase-1 gene (SOD1)
known as A4V (Alanine for Valine substitution at point 4). He
enters data about his disease progression to-date using a
self-report functional outcome scale on his profile in the online
community.
[0129] An algorithm compares the likelihood of the patient reaching
a given clinical milestone (e.g. needing a wheelchair, needing a
ventilator, needing to use assistive technology to communicate) by
creating a model comparing him to other patients with ALS that are
similar in background and also have an A4V mutation.
[0130] The algorithm can be based on the ALSFRS. Typical rates of
progression are linear with most patients progressing at a rate of
1 point per month. However, analysis of the dataset suggest
patients with an A4V SOD1 mutation progress at a rate of 3 points
per month.
[0131] The patient is presented with the option to see when certain
health outcomes are likely to occur. If he decides to see the
predicted results, he is shown a description of the system we have
used to estimate the outcome. He then clicks on "proceed" and is
shown his current progression plot with an overlaid curve
progressing from the most recent datapoint/present time, to the
predicted health outcomes. Rather than a simple line, the curve
presents bands of varying widths according to (i) the quantity and
quality of data provided by the individual and (i) the quantity and
quality of data provided by other individuals like him in the
system.
[0132] As the time of the predicted outcome approaches, the user is
sent a private message asking him to validate the accuracy of the
prediction made with regards to his health outcome, e.g., "In the
past, you used our predictive outcome system to help you understand
when you might need a wheelchair. At the time, our model of your
disease progress suggested your disease state (as measured by your
ALSFRS) would be 22 and you might need a wheelchair around 5 months
from now. Please answer the FRS questionnaire. And, do you now use
a wheelchair? If so please click "yes" and let us know from what
date you started using a wheelchair. If not, please click "no". We
will ask you again in 3 months' time."
[0133] Positive feedback gained from members decreases the
confidence intervals surrounding predictions for a similar group of
patients, i.e. in this example, future 55-year old male ALS
patients with a SOD1 A4V mutation will see a narrower confidence
interval around the predicted datapoint of needing a wheelchair.
Negative feedback will lead to an increased confidence
interval.
[0134] Members of scientific staff can evaluate the quality and
confidence inherent in a particular model through use of a system
tool viewable only by administrators of the website. Models with
consistently poor feedback can be examined in detail and altered
manually to improve performance.
Example 2: Excessive Gambling in Parkinson's Disease
[0135] A 75-year old male diagnosed with Parkinson's disease enters
data stating that he has recently been prescribed the drug
MIRAPEX.RTM. (pramipexol) at a rate of 2 mg per day. He enters data
about his treatment regimen using a data-entry module which records
his drug regime on his profile in the online community. He enters
data that he has had a previous history of alcohol abuse,
depression, and gambling.
[0136] An algorithm compares the likelihood of the patient reaching
a given clinical milestone (e.g., developing a known side effect
from the drug (pathological gambling), developing tolerance to the
drug and needing a higher dose, finding an improvement in his
physical health) by creating a model comparing him to other
patients with Parkinson's disease that are similar in background
and are also taking the same drug at a similar dosage.
[0137] The patient and other patients in the population enter data
about at least two medical condition metrics. First, the patients
self-report the severity of their Parkinson's Diseases by using a
scale such as the Unified Parkinson's Disease Rating Scale (UPDRS).
Second, the patient enter metrics to track problem gambling, the
known side effect of pramipexol. Various scales exist to quantify
problem gambling including the South Oaks Gambling Screen (SOGS),
the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV),
and the Canadian Problem Gambling Severity Index (PGSI).
[0138] The patient is presented with the option to see whether
certain health outcomes are likely to occur. If he decides to see
the predicted results he is shown a description of the system we
have used to come to estimate the outcome. He then clicks on
"proceed" and is shown the current likelihood of experiencing side
effects from the drug on the basis of known data from the clinical
literature and/or from other members of the site. Rather than a
simple number or percentage chance, the patient is presented with a
spectrum of likelihood of varying widths according to (i) the
quantity and quality of data provided by the individual and (ii)
the quantity and quality of data provided by other individuals like
him in the system.
[0139] As the time of the predicted outcome approaches, the user is
sent a private message asking him to validate the accuracy of the
prediction made with regards to his health outcome, e.g., "In the
past, you used our predictive outcome system to help you understand
whether you might experience a known side effect of MIRAPEX.RTM.,
excessive gambling. At the time, our model of your disease progress
suggested you had around a 20-40% chance of developing excessive
gambling in the next 12 months. Have you found this to be true? If
so please click "yes" and let us know from what date you started
gambling excessively. If not, please click "no". We will ask you
again in 3 months' time."
[0140] Positive feedback gained from members decreases the
confidence intervals surrounding predictions for a similar group of
patients, i.e. in this example, future 75-year old male Parkinson's
disease patients with a history of alcoholism, depression, and
gambling will see a narrower confidence interval around the
predicted datapoint of developing a gambling problem. Negative
feedback will lead to an increased confidence interval.
[0141] Members of scientific staff can evaluate the quality and
confidence inherent in a particular model through use of a system
tool viewable only by administrators of the website. Models with
consistently poor feedback can be examined in detail and altered
manually to improve performance.
Example 3: Rate of Progression in Huntington's Disease
[0142] A 38-year old male with a clinical diagnosis of Huntington's
disease enters data stating that genetic testing by his clinician
reveals that he has a relatively low number of pathological CAG
repeats on the Huntington gene, having only 40 triple repeats. He
enters data that he has a high level of education, a high
socio-economic status, and a large family able to support him.
[0143] An algorithm evaluates the likelihood of him having to be
looked after in a nursing home by creating a model comparing him to
other patients with Huntington's disease that have a similar number
of CAG repeats and also have a high level of education, a high
socio-economic status, and a large family able to support him.
[0144] The algorithm can use self-reported of functionally ability
from other population members as assessed by the Huntington's
Disease Rating Scale (HDRS) to predict the patient's future
functional ability.
[0145] The patient is presented with the option to see when certain
health outcomes are likely to occur. If he decides to see the
predicted results, he is shown a description of the system we have
used to come to estimate the outcome. He then clicks on "proceed"
and is shown the current likelihood of having to be cared for in a
nursing home on the basis of known data from the clinical
literature and/or from other members of the site. Rather than a
simple number or percentage chance, the patient is presented with a
spectrum of likelihood of varying widths according to (i) the
quantity and quality of data provided by the individual and (ii)
the quantity and quality of data provided by other individuals like
him in the system.
[0146] As the time of the predicted outcome approaches, the user is
sent a private message asking him to validate the accuracy of the
prediction made with regards to his health outcome, i.e. "In the
past, you used our predictive outcome system to help you understand
whether you might need to be looked after in a care home. At the
time, our model of your disease progress and family support
suggested you had around a 2% chance of needing to be in a care
home in the next 12 months. Have you found this to be true? If you
did need to be in a care home please click "no" and let us know
from what date you started being looked after in a care home. If
not, please click "yes". We will ask you again in 12 months'
time."
[0147] Positive feedback gained from members decreases the
confidence intervals surrounding predictions for a similar group of
patients, i.e. in this example, future 38-year old male
Huntington's disease patients a high level of education, a high
socio-economic status, and a large family able to support them will
see a narrower confidence interval around the predicted datapoint
of needing to be looked after in a care home. Negative feedback
will lead to an increased confidence interval.
[0148] Members of scientific staff can evaluate the quality and
confidence inherent in a particular model through use of a system
tool viewable only by administrators of the website. Models with
consistently poor feedback can be examined in detail and altered
manually to improve performance.
Application to Depression
[0149] Some modern theories of depression posit that depression
results from cognitive distortions. While all individuals become
sad or upset at some points in time, most individuals have the
perspective to recognize that such feeling are short-lived.
However, individuals with a major depressive disorder are thought
by some to lack the ability to recognize recall a time before they
entered a depressive episode, and therefore cannot anticipate
better times in the future.
[0150] The invention described herein are capable of helping
persons dealing with depression. Depressed persons can enter their
mood or other medical condition metrics into the systems described
herein and retrieve graphical representations of these metrics over
time. Such a system provides external memory and perspective for
the patient.
[0151] Furthermore, the inventions described herein can be used by
generally healthy individuals in advance of disease. For example,
military personnel can record medical condition metrics before
deployment to an armed conflict. Such prior medical condition
metrics can serve both as a reference point for the military
personnel when coping with conditions such a post traumatic stress
disorder (PTSD) and to military health personnel seeking to screen
for PTSD.
[0152] The functions of several elements can, in alternative
embodiments, be carried out by fewer elements, or a single element.
Similarly, any functional element can perform fewer, or different,
operations than those described with respect to the illustrated
embodiment. Also, functional elements (e.g., modules, databases,
computers, clients, servers, and the like) shown as distinct for
purposes of illustration can be incorporated within other
functional elements, separated in different hardware or distributed
in a particular implementation.
[0153] While certain embodiments according to the invention have
been described, the invention is not limited to just the described
embodiments. Various changes and/or modifications can be made to
any of the described embodiments without departing from the spirit
or scope of the invention. Also, various combinations of elements,
steps, features, and/or aspects of the described embodiments are
possible and contemplated even if such combinations are not
expressly identified herein.
INCORPORATION BY REFERENCE
[0154] All patents, published patent applications, and other
references disclosed herein are hereby expressly incorporated by
reference in their entireties by reference.
EQUIVALENTS
[0155] Those skilled in the art will recognize, or be able to
ascertain using no more than routine experimentation, many
equivalents of the specific embodiments of the invention described
herein. Specifically, although this application periodically
discusses the application of the invention to "diseases", the
invention is equally applicable to other medical events such as
aging, fertility, and the like. Such equivalents are intended to be
encompassed by the following claims.
* * * * *
References