U.S. patent application number 13/826441 was filed with the patent office on 2013-09-05 for systems and methods for transitioning patient care from signal-based monitoring to risk-based monitoring.
The applicant listed for this patent is Dimitar V. Baronov, Evan J. Butler, Jesse M. Lock, Michael F. McManus. Invention is credited to Dimitar V. Baronov, Evan J. Butler, Jesse M. Lock, Michael F. McManus.
Application Number | 20130231949 13/826441 |
Document ID | / |
Family ID | 49043353 |
Filed Date | 2013-09-05 |
United States Patent
Application |
20130231949 |
Kind Code |
A1 |
Baronov; Dimitar V. ; et
al. |
September 5, 2013 |
SYSTEMS AND METHODS FOR TRANSITIONING PATIENT CARE FROM
SIGNAL-BASED MONITORING TO RISK-BASED MONITORING
Abstract
A risk-based patient monitoring system for critical care
patients combines data from multiple sources to assess the current
and the future risks to the patient, thereby enabling providers to
review a current patient risk profile and to continuously track a
clinical trajectory. A physiology observer module in the system
utilizes multiple measurements to estimate Probability Density
Functions (PDF) of a number of Internal State Variables (ISVs) that
describe a components of the physiology relevant to the patient
treatment and condition. A clinical trajectory interpreter module
in the system utilizes the estimated PDFs of ISVs to identify under
which probable patient states the patient can be currently
categorized and assign a probability value that the patient will be
in each of the identified states. The combination of patient states
and their probabilities is defined as the clinical risk to the
patient.
Inventors: |
Baronov; Dimitar V.;
(Allston, MA) ; Butler; Evan J.; (New Haven,
CT) ; Lock; Jesse M.; (Winchester, MA) ;
McManus; Michael F.; (Halifax, MA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Baronov; Dimitar V.
Butler; Evan J.
Lock; Jesse M.
McManus; Michael F. |
Allston
New Haven
Winchester
Halifax |
MA
CT
MA
MA |
US
US
US
US |
|
|
Family ID: |
49043353 |
Appl. No.: |
13/826441 |
Filed: |
March 14, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13689029 |
Nov 29, 2012 |
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13826441 |
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13328411 |
Dec 16, 2011 |
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13689029 |
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61774274 |
Mar 7, 2013 |
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61727820 |
Nov 19, 2012 |
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61699492 |
Sep 11, 2012 |
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61684241 |
Aug 17, 2012 |
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61620144 |
Apr 4, 2012 |
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61614861 |
Mar 23, 2012 |
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61614846 |
Mar 23, 2012 |
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Current U.S.
Class: |
705/2 |
Current CPC
Class: |
A61B 5/0402 20130101;
G16H 50/30 20180101; G16H 50/50 20180101; A61B 5/14542 20130101;
G16H 50/20 20180101; A61B 5/14546 20130101; A61B 5/14532 20130101;
A61B 5/7275 20130101; A61B 5/0205 20130101 |
Class at
Publication: |
705/2 |
International
Class: |
G06F 19/00 20060101
G06F019/00 |
Goverment Interests
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0010] This invention was made with government support under
R43HL117340 awarded by the National Heart, Lung, And Blood
Institute of the National Institutes of Health. The government has
certain rights in the invention.
Claims
1. A computer-implemented method for risk-based monitoring of
patients, comprising: acquiring, with a computer, data associated
with a plurality of the internal state variables each describing a
parameter physiologically relevant to at least one of a treatment
and a condition of a patient; storing, in a computer accessible
memory, the acquired data associated with the plurality of the
internal state variables; generating, with a computer, estimated
probability density functions for the plurality of the internal
state variables; identifying, with a computer, from the generated
probability density functions of the internal state variables, into
which of a first plurality of possible patient states the patient
is currently categorizable and; generating a probability value
associated with each identified possible patient state.
2. The method of claim 1, wherein the probability value associated
with the identified possible patient states is between 0% and
100%.
3. The method of claim 1, further comprising: presenting, through a
user interface, the probability values and their associated
respective identified possible patient states.
4. The method of claim 1, further comprising: assigning a hazard
level associated with each of the identified possible patient
states, and presenting the probability values and hazard levels
associated with the respective identified possible patient
states.
5. The method of claim 1, wherein generating, with a computer,
estimated probability density functions for the plurality of the
internal state variables comprises: generating estimated
probability density functions for the first plurality of the
internal state variables at a time step t.sub.k; and generating
probability density functions for the plurality of the internal
state variables at another time step t.sub.k+1 from the probability
density functions generated at a time step t.sub.k.
6. The method of claim 5, wherein each of the received measurements
of respective of the internal state variables are associated with a
same time step.
7. The method of claim 5, wherein not all of the received
measurements of respective of the internal state variables are
associated with a same time step.
8. The method of claim 1, wherein generating, with a computer,
estimated probability density functions for the plurality of the
internal state variables comprises: comparing a newly received
measurement associated with an the internal state variable with a
predetermined predicted likelihood of probable measurements given
previously received measurements; and not incorporating the newly
received measurement into the estimated probability density
function for the associated internal state variable, if the newly
received measurement is not within the predetermined predicted
likelihood of probable measurements for the associated internal
state variable.
9. The method of claim 1, wherein identifying a first plurality of
possible patient states and generating a probability value
associated with each identified possible patient state comprise:
receiving, from a source, external computational data in the form
of a probability value associated with a new attribute describing a
patient state not within the first plurality of possible patient
states; and identifying, with a computer, from the generated
probability density functions of the internal state variables and
the probability value associated with the new attribute, into which
of a second plurality of possible patient states, the patient is
currently categorizable; and generating a probability value
associated with each identified possible patient states.
10. The method of claim 1, wherein generating, with a computer,
estimated probability density functions for the plurality of the
internal state variables comprises: generating estimated
probability density functions for the first plurality of the
internal state variables at a time step t.sub.k; receiving, from a
source, external computational data associated with a particular
one of the plurality of the internal state variables; and
generating probability density functions for the plurality of the
internal state variables at another time step t.sub.k+1 from the
probability density functions generated at a time step t.sub.k and
from received measurements associated with respective of the
internal state variables and the external computational data
associated with the particular one of the plurality of the internal
state variables.
11. A risk based monitoring system for monitoring patients,
comprising: a processor; a memory coupled to the processor; a data
reception module, operably coupled to a plurality of sources of
information relative to a patient, for acquiring data associated
with a plurality of the internal state variables each describing a
parameter physiologically relevant to at least one of a treatment
and a condition of a patient; a physiology observer module, in
communication with the data reception module, for generating
probability density functions of the internal state variables; a
clinical trajectory interpreter module, in communication with the
physiology observer module, for identifying into which of a first
plurality of possible patient states the patient is currently
categorizable and for generating a probability value associated
with each identified possible patient state.
12. The system of claim 11, further comprising: a user interaction
module, in communication with the clinical trajectory interpreter
and memory, for presenting the probability values and their
associated respective identified possible patient states.
13. The system of claim 11, wherein the physiology observer module
further comprises: a dynamic model and an observation model stored
in the memory.
14. The system of claim 13, wherein the physiology observer module
further comprises: an inference engine configured to interoperate
with the dynamic model and the observation model is stored in
memory.
15. The system of claim 13, wherein the physiology observer module
has a predictive mode of operation in which the estimated
probability density functions for the plurality of the internal
state variables at a time step t.sub.k, are provided to the dynamic
model, to produce estimated probability density functions for the
plurality of the internal state variables at another time step
t.sub.k+1.
16. The system of claim 14, wherein not all of the received
measurements of respective of the internal state variables are
associated with a same time step.
17. The system of claim 11, wherein the physiology observer module
compares a newly received measurement of an the internal state
variable with a predetermined predicted likelihood of probable
measurements given previously received measurements, and does not
incorporate the newly received measurement into the estimated
probability density function for the associated internal state
variable, if the newly received measurement is not within the
predetermined predicted likelihood of probable measurements for the
associated internal state variable.
18. The system of claim 11, wherein the clinical trajectory
interpreter module receives external computational data in the form
of a probability value associated with a new attribute describing a
patient state not within the first plurality of possible patient
states, and identifies, from the generated probability density
functions of the internal state variables and the probability value
associated with the new attribute, into which of a second plurality
of possible patient states, the patient is currently categorizable
and for generating a probability value associated with each
identified possible patient state.
19. The system of claim 11, wherein the physiology observer module
generates estimated probability density functions for the first
plurality of the internal state variables at a time step t.sub.k
and generates probability density functions for the plurality of
the internal state variables at another time step t.sub.k+1 from
the probability density functions generated in at a time step
t.sub.k and from received measurements associated with respective
of the internal state variables and from external computational
data associated with the particular one of the plurality of the
internal state variables.
20. A computer program product comprising a non-transitory
computer-readable medium having executable instructions in the form
of computer program code stored thereon comprising: computer
program code for acquiring data associated with a plurality of the
internal state variables each describing a parameter
physiologically relevant to at least one of a treatment and a
condition of a patient; computer program code for storing the
acquired data associated with the plurality of the internal state
variables; computer program code for generating estimated
probability density functions for the plurality of the internal
state variables; and computer program code for identifying from the
generated probability density functions of the internal state
variables, which of a plurality of possible patient states the
patient is currently categorizable and generating a probability
value associated with each identified patient state.
21. The computer program product of claim 20, wherein the
probability value associated with the identified possible patient
states is between 0% and 100%.
22. The computer program product of claim 20, further comprising:
computer program code for presenting the probability values and
their associated respective identified patient states.
23. The computer program product of claim 20, further comprising:
computer program code for assigning a hazard level associated with
each of identified possible patient states, and computer program
code for presenting the probability values and hazard levels
associated the respective identified possible patient states.
24. The computer program product of claim 20, wherein computer
program code for generating estimated probability density functions
for the plurality of the internal state variables comprises:
computer program code for generating estimated probability density
functions for the first plurality of the internal state variables
at a time step t.sub.k; and computer program code for generating
probability density functions for the plurality of the internal
state variables at another time step t.sub.k+1 from the probability
density functions generated at a time step t.sub.k.
25. The computer program product of claim 20, wherein not all of
the received measurements of respective of the internal state
variables are associated with a same time step.
26. A computer-implemented method for risk based monitoring of
patients, comprising: acquiring, with a computer, data associated
with a plurality of the internal state variables each describing a
parameter physiologically relevant to one of a treatment and a
condition of a patient, not all of the data associated with the
plurality of the internal state variables with at the same
periodicity; storing, in a computer accessible memory, the acquired
data associated with the plurality of the internal state variables;
generating, with a computer, estimated probability density
functions for the plurality of the internal state variables; and
identifying, with a computer, from the generated probability
density functions of the internal state variables, into which of a
first plurality of possible patient states, the patient could has
previously been categorizable and generating a probability value
associated with each identified possible prior patient state.
27. The method of claim 26, wherein generating, with a computer,
estimated probability density functions for the plurality of the
internal state variables comprises: generating estimated
probability density functions for the first plurality of the
internal state variables at a current time step t.sub.k; and
generating probability density functions for the plurality of the
internal state variables at another time step t.sub.k-N, where N is
an integer value greater than 1, by evolving backwards from the
probability estimates at time step t.sub.k to the time step
t.sub.k-N using a defined transition probability kernel.
28. The method of claim 1, wherein a second plurality of the
internal state variables each describing a parameter
physiologically relevant to one of a treatment and a condition of a
patient have no acquired data associated therewith and wherein
generating, with a computer, estimated probability density
functions for the plurality of the internal state variables
comprises: generating estimated probability density functions for
the second plurality of the internal state variables at a time step
t.sub.k; and generating probability density functions for the
second plurality of the internal state variables at time step
t.sub.k+1 from the probability density functions generated at a
time step t.sub.k and from probability density functions associated
with other internal state variables at a time step t.sub.k.
29. The method of claim 5, wherein generating probability density
functions for the plurality of the internal state variables at
another time step t.sub.k+1 further comprises generating the
probability density functions from received measurements associated
with internal state variables.
30. The method of claim 24, wherein computer program code for
generating probability density functions for the plurality of the
internal state variables at another time step t.sub.k+1 from the
probability density functions generated at a time step t.sub.k
further comprises generating the probability density functions for
the plurality of the internal state variables at another time step
t.sub.k+1 from received measurements of respective of the internal
state variables
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority to the
following non-provisional patent applications: [0002] U.S. patent
application Ser. No. 13/689,029, filed on Nov. 29, 2012, entitled
SYSTEMS AND METHODS FOR OPTIMIZING MEDICAL CARE THROUGH DATA
MONITORING AND FEEDBACK TREATMENT, Attorney Docket No. 44429-00100
CON; and [0003] U.S. application Ser. No. 13/328,411, filed on Dec.
16, 2011, entitled METHOD AND APPARATUS FOR VISUALIZING THE
RESPONSE OF A COMPLEX SYSTEM TO CHANGES IN A PLURALITY OF INPUTS,
Attorney Docket No. 44429-00104; and to the following provisional
patent applications: [0004] U.S. Provisional Application No.
61/727,820, filed on Nov. 19, 2012, entitled USER INTERFACE DESIGN
FOR RAHM, Attorney Docket No. 44429-00108 PROV; [0005] U.S.
Provisional Application No. 61/699,492, filed on Sep. 11, 2012,
entitled SYSTEMS AND METHODS FOR EVALUATING CLINICAL TRAJECTORIES
AND TREATMENT STRATEGIES FOR OUTPATIENT CARE, Attorney Docket No.
44429-00107 PROV; [0006] U.S. Provisional Application No.
61/684,241, filed on Aug. 17, 2012, entitled SYSTEM AND METHODS FOR
PROVIDING RISK ASSESSMENT IN ASSISTING CLINICIANS WITH EFFICIENT
AND EFFECTIVE BLOOD MANAGEMENT, Attorney Docket No. 44429-00106
PROV; [0007] U.S. Provisional Application No. 61/620,144, filed on
Apr. 4, 2012, entitled SYSTEMS AND METHODS FOR PROVIDING MOBILE
ADVANCED CARDIAC SUPPORT, Attorney Docket No. 44429-00103 PROV;
[0008] U.S. Provisional Application No. 61/614,861, filed on Mar.
23, 2012 entitled SYSTEMS AND METHODS FOR REDUCING MORBIDITY AND
MORTALITY WHILE REDUCING LENGTH OF STAY IN A HOSPITAL SETTING,
Attorney Docket No. 44429-00102 PROV; and [0009] U.S. Provisional
Application No. 61/774,274, filed on Mar. 7, 2013, entitled SYSTEMS
AND METHODS FOR TRANSITIONING PATIENT CARE FROM SIGNAL-BASED
MONITORING TO RISK-BASED MONITORING, Attorney Docket No.
44429-00101 PROV II, the entire subject matter of each of the
foregoing applications being incorporated herein by this reference
for all purposes.
BACKGROUND
[0011] The present disclosure relates to systems and methods for
risk-based patient monitoring. More particularly, the present
disclosure relates to systems and methods for assessing the current
and future risks of a patient by combining data of the patient from
various different sources.
[0012] Practicing medicine is becoming increasingly more
complicated due to the introduction of new sensors and treatments.
As a result, clinicians are confronted with an avalanche of patient
data, which needs to be evaluated and well understood in order to
prescribe the optimal treatment from the multitude of available
options, while reducing patient risks. One environment where this
avalanche of information has become increasingly problematic is the
Intensive Care Unit (ICU). There, the experience of the attending
physician and the physician's ability to assimilate the available
physiologic information have a strong impact on the clinical
outcome. It has been determined that hospitals which do not
maintain trained intensivists around the clock experience a 14.4%
mortality rate as opposed to a 6.0% rate for fully staffed centers.
It is estimated that raising the level of care to that of average
trained physicians across all ICUs can save 160,000 lives and $4.3
Bn annually. As of 2012, there is a shortage of intensivists, and
projections estimate the shortage will only worsen, reaching a
level of 35% by 2020.
[0013] The value of experience in critical care can be explained by
the fact that clinical data in the ICU is delivered at a rate far
greater than even the most talented physician can absorb, and
studies have shown that errors are six times more likely under
conditions of information overload and eleven time more likely with
an acute time shortage. Moreover, treatment decisions in the ICU
heavily rely on clinical signs that are not directly measurable,
but are inferred from other physiologic information. Thus clinician
expertise and background play a more significant role in the minute
to minute decision making process. Not surprisingly, this leads to
a large variance in hidden parameter estimation. As an example,
although numerous proxies for cardiac output are continuously
monitored in critical care, studies have demonstrated poor
correlation between subjective assessment by clinicians, and
objective measurement by thermodilution. Experienced intensivists
incorporate this inherent uncertainty in their decision process by
effectively conducting risk management, i.e. prescribing the
treatment not only based on the most probable patient state, but
also weighing in the risks of the patient being in other more
adverse states. From this perspective, experienced intensivists
confront the data overload in intensive care by converting the
numerous heterogeneous signals from patient observations into a
risk assessment.
[0014] Therefore, there is a clear need for a decision support
system in the ICU that achieves a paradigm shift from signal-based
patient monitoring to risk based patient monitoring, and
consequently helps physicians overcome the barrage of data in the
ICU.
BRIEF SUMMARY
[0015] Disclosed herein is a risk-based patient monitoring system
for critical care patients that combines data from any of bedside
monitors, electronic medical records, and other patient specific
information, to assess the current and the future risks to the
patient. The system may be also embodied as a decision support
system that prompts the user with specific actions according to a
standardized medical plan, when patient specific risks pass a
predefined threshold. Yet another embodiment of the described
technologies is an outpatient monitoring system which combines
patient and family evaluation, together with information about
medication regiments and physician evaluations to produce a risk
profile of the patient, continuously track its clinical trajectory,
and provide decision support to clinicians regarding when to
schedule a visit or additional tests.
[0016] According to one implementation, a risk based monitoring
application executing on a system processor comprises a data
reception module, a physiology observer module, a clinical
trajectory interpreter module, and a visualization and user
interaction module. In an exemplary embodiment, the data reception
module may be configured to receive data from bedside monitors,
electronic medical records, treatment device, and any other
information that may be deemed relevant to make informed assessment
regarding the patient's clinical risks, and any combination thereof
of the preceding elements.
[0017] The physiology observer module utilizes multiple
measurements to estimate Probability Density Functions (PDF) of
Internal State Variables (ISVs) that describe the components of the
physiology relevant to the patient treatment and condition. The
clinical trajectory interpreter module may be configured with
multiple possible patient states, and determine which of those
patient states are probable and with what probability, given the
estimated probability density functions of the internal state
variables.
[0018] In various embodiments, the clinical trajectory interpreter
module determines the patient conditions under which a patient may
be categorized and is capable of also determining the probable
patient states under which the patient can be currently
categorized, given the estimated probability density functions of
the internal state variables. In this way, each of the possible
patient states is assigned a probability value from 0 to 1. The
combination of patient states and their probabilities is defined as
the clinical risk to the patient.
[0019] The visualization and user interactions module takes i) time
series of physiologic measurements acquired continuously or
intermittently and patient specific identifiers such as condition,
demographics, visual examinations from the data reception module;
ii) time series of probability density functions of internal state
variables estimated from the physiology observer module; and time
series of the probabilities that the patient is at particular state
and the hazard level of the respective risks from the clinical
trajectory interpreter module. Then it visualizes this data on
graphs which represent the dependence of the variables with time,
by either directly plotting them on a screen, or in the case of
probability density functions plotting them by encoding the
likelihood at particular point of time and at particular value with
a color scheme. The visualization and user interactions module may
also visualize the current risks to the patient by representing
them with boxes of different size and color, the size of the box
corresponding to the probability of a patient state at particular
point in time and the color of the box corresponding to its hazard
level. Additionally, the visualization and user interactions module
can allow the users to set alarms based on the patient state
probabilities, share those alarms with other users, take notes
related to the patient risks and share those notes with other
users, and browse other elements of the patient medical
history.
[0020] According to one aspect of the disclosure, a
computer-implemented medium and method for risk based monitoring of
patients comprises: A) acquiring, with a computer, data associated
with a plurality of the internal state variables each describing a
parameter physiologically relevant to one of a treatment and a
condition of a patient; B) storing, in a computer accessible
memory, the acquired data associated with the plurality of the
internal state variables; C) generating, with a computer, estimated
probability density functions for the plurality of the internal
state variables; and D) identifying, with a computer, from the
generated probability density functions of the internal state
variables, into which of a plurality of possible patient states the
patient is currently categorizable and generating a probability
value associated with each identified possible patient state. In
one embodiment, the probability value associated with the
identified possible patient states is between 0 and 1. In another
embodiment, the method further comprises: E) presenting on a screen
the probability values and their associated respective identified
possible patient states, wherein the combination of identified
possible patient states and their associated respective probability
values is defined as the clinical risk to the patient.
[0021] According to another aspect of the disclosure, a risk based
monitoring system for monitoring patients comprises: a processor; a
memory coupled to the processor; a data reception module, operably
coupled to a plurality of sources of information relative to a
patient, for acquiring data associated with a plurality of the
internal state variables each describing a parameter
physiologically relevant to one of a treatment and a condition of a
patient; a physiology observer module, in communication with the
data reception module, and configured to generate probability
density functions of the internal state variables; a clinical
trajectory interpreter module, in communication with the physiology
observer module, and configured to identify into which of a
plurality of possible patient states the patient is currently
categorizable and to generate a probability value associated with
each identified possible patient state. In one embodiment, the
method further comprises: a user interaction module, in
communication with the clinical trajectory interpreter and the data
reception module and memory, for presenting the probability values
and their associated respective identified possible patient states,
wherein the combination of identified possible patient states and
the associated respective probability values is defined as the
clinical risk to the patient.
[0022] According to still other aspects of the disclosure, certain
measurements, such as Hemoglobin, are available to the system with
an unknown amount of time latency, meaning the measurements are
valid in the past relative to the current time and the time they
arrive over the data communication links. The physiology observer
module may handle such out of sequence measurements using back
propagation, in which the current estimates of the ISVs are
projected back in time to the time of validity of the measurements,
so that the information from the latent measurement can be
incorporated correctly. Accordingly, in accordance with another
aspect of the disclosure, a computer-implemented method for risk
based monitoring of patients, comprises: A) acquiring, with a
computer, data associated with a plurality of the internal state
variables each describing a parameter physiologically relevant to
one of a treatment and a condition of a patient, not all of the
data associated with the plurality of the internal state variables
with at the same periodicity; B) storing, in a computer accessible
memory, the acquired data associated with the plurality of the
internal state variables; C) generating, with a computer, estimated
probability density functions for the plurality of the internal
state variables; and D) identifying, with a computer, from the
generated probability density functions of the internal state
variables, into which of a first plurality of possible patient
states P(S.sub.1), P(S.sub.2), P(S.sub.3), . . . , P(S.sub.n), the
patient could has previously been categorizable and generating a
probability value associated with each identified possible prior
patient state. In one embodiment, generating estimated probability
density functions comprises: C1) generating estimated probability
density functions for the first plurality of the internal state
variables at a current time step t.sub.k; and C2) generating
probability density functions for the plurality of the internal
state variables at a another time step t.sub.k-N, where N is an
integer value greater than 1, by evolving backwards from the
probability estimates at time step t.sub.k to the time step
t.sub.k-N using a defined transition probability kernel.
BRIEF DESCRIPTION OF THE DRAWINGS
[0023] It should be understood at the outset that although
illustrative implementations of one or more embodiments of the
present disclosure are provided below, the disclosed systems and/or
methods may be implemented using any number of techniques, whether
currently known or in existence. The disclosure should in no way be
limited to the illustrative implementations, drawings, and
techniques illustrated below, including the exemplary designs and
implementations illustrated and described herein, but may be
modified within the scope of the appended claims along with their
full scope of equivalents.
[0024] In the drawings;
[0025] FIG. 1 illustrates conceptually a medical care risk based
monitoring environment in accordance with the disclosure;
[0026] FIG. 2A illustrates conceptually a basic schematic of the
physiology observer module in accordance with the disclosure;
[0027] FIGS. 2B-D illustrate conceptually exemplary graphs of
probability density functions for select ISVs as generated by the
physiology observer module in accordance with the disclosure;
[0028] FIG. 3 illustrates conceptually a non-limiting example of a
physiology observer process in accordance with the disclosure;
[0029] FIG. 4 illustrates conceptually a non-limiting example of
the physiology observer process in accordance with the
disclosure;
[0030] FIG. 5 illustrates conceptually a time line, wherein back
propagation is used to incorporate information in accordance with
the disclosure;
[0031] FIG. 6 illustrates conceptually an example of a process
involving mean arterial blood pressure (ABPm) in accordance with
the disclosure;
[0032] FIG. 7 illustrates conceptually an example of resampling in
accordance with the disclosure;
[0033] FIG. 8 illustrates conceptually a clinical trajectory
interpreter module using joined Probability Density Functions of
ISVs and performing state probability estimation to calculate the
probabilities of different patient states in accordance with the
disclosure;
[0034] FIG. 9 illustrates conceptually a non-limiting example of a
definition of a patient state employed by the clinical trajectory
interpreter module in accordance with the disclosure;
[0035] FIG. 10 illustrates conceptually a non-limiting example of
how a clinical trajectory interpreter module may employ the
definition of patient states to assign probabilities that the
patient may be classified under each of the four possible patient
states at a particular point of time;
[0036] FIG. 11 illustrates conceptually an alternative approach of
estimating the probabilities for different patient states in
accordance with the disclosure;
[0037] FIG. 12 illustrates conceptually a non-limiting example of a
definition of patient states assigned with hazard levels by the
clinical trajectory interpreter module in accordance with the
disclosure;
[0038] FIG. 13 illustrates conceptually patient states and their
respective probabilities organized into tree graphs called
etiologies in accordance with the disclosure;
[0039] FIG. 14 illustrates conceptually an exemplary etiology tree
for a given set of patient states and physiologic variables in
accordance with the disclosure;
[0040] FIG. 15 illustrates conceptually a method for calculating
the utility of different measurements in accordance with the
disclosure;
[0041] FIG. 16 illustrates conceptually one possible realization of
integration of external computation generated from third party
algorithms in accordance with the disclosure;
[0042] FIG. 17 illustrates conceptually an example of integration
instructions of an external computation in accordance with the
disclosure;
[0043] FIG. 18 illustrates conceptually an additional example of
integration instructions of an external computation in accordance
with the disclosure;
[0044] FIG. 19 illustrates conceptually example functionalities of
the visualization and user interactions module in accordance with
the disclosure;
[0045] FIG. 20 illustrates conceptually an example of a summary
view that may convey on a single screen a risk profile for each
patient in a particular hospital unit in accordance with the
disclosure;
[0046] FIG. 21 illustrates conceptually one possible realization of
a view describing the ongoing risks of the patient in accordance
with the disclosure;
[0047] FIG. 22 illustrates conceptually how the slider on top of
the patient view may be utilized in reviewing the history of the
patient risks in accordance with the disclosure;
[0048] FIG. 23 illustrates how the user can navigate the etiology
tree by clicking on the composite patient state and viewing the
constituent patient states in accordance with the disclosure;
[0049] FIG. 24 illustrates conceptually how in the same framework
the user may view the predicted risks for the patient by sliding
the slider ahead of current time in accordance with the
disclosure;
[0050] FIG. 25 illustrates conceptually how the user may choose to
set an alarm for a particular risk in accordance with the
disclosure;
[0051] FIG. 26 illustrates conceptually yet another possible
visualization of the patient risk trajectory, i.e., the evolution
of the patient states' probabilities associated with particular
risks in accordance with the disclosure;
[0052] FIG. 27 illustrates conceptually how the system may directly
visualize the probability density functions of various internal
state variables in accordance with the disclosure;
[0053] FIG. 28 illustrates conceptually an example of a tagging
feature that a user interface may implement in accordance with the
disclosure;
[0054] FIG. 29 illustrates conceptually a Newsfeed view of the user
interface in accordance with the disclosure;
[0055] FIG. 30 illustrates conceptually a Condition Summary View of
the user interface in accordance with the disclosure;
[0056] FIG. 31 illustrates conceptually the ability for the user
interface to include and display reference material, which may be
accessed through the Internet; or stored within the system in
accordance with the disclosure;
[0057] FIG. 32 illustrates conceptually a general Dynamic Bayesian
Network (DBN) that may be employed to capture the physiology model
of the HLHS stage 1 palliation patients in accordance with the
disclosure;
[0058] FIG. 33 illustrates conceptually several equations that may
be used to model the dynamics of the HLHS stage 1 physiology in
accordance with the disclosure;
[0059] FIG. 34 illustrates conceptually example equations that may
be used to abstract the relationships between the dynamic variables
in the model and the derived variables in accordance with the
disclosure;
[0060] FIG. 35 illustrates conceptually a possible observation
model that may be used to relate the derived variables with the
available sensor data in accordance with the disclosure;
[0061] FIG. 36 illustrates conceptually possible attributes,
patient states, and etiology tree that may be used by the clinical
trajectory interpreter module in the case of the HLHS Stage 1
population in accordance with the disclosure;
[0062] FIG. 37 illustrates conceptually one possible environment in
which the risk based monitoring system can be applied to assist
clinicians in deciding whether to apply a particular treatment in
accordance with the disclosure;
[0063] FIG. 38 illustrates conceptually a non-limiting example set
of patient states relevant to blood transfusion that may be used to
inform the blood transfusion decision in accordance with the
disclosure;
[0064] FIG. 39 illustrates conceptually another application of the
risk-based monitoring system, applying standardized medical plans
in accordance with the disclosure;
[0065] FIG. 40 illustrates conceptually an example application of
the risk based monitoring system combined with a specific type of
standardized clinical plan in accordance with the disclosure;
[0066] FIG. 41 illustrates conceptually an example risk
stratification that may be employed by the system in the context of
Nitric Oxide treatment in accordance with the disclosure;
[0067] FIG. 42 illustrates conceptually possible patient states
that may describe the clinical trajectory of an ADHD patient in
accordance with the disclosure;
[0068] FIG. 43 lists the available patient evaluation modalities as
M1, M2, and M3;
[0069] FIG. 44 illustrates conceptually a dynamic model of the
patient evolution from state to state abstracted by a Dynamic
Bayesian Network in accordance with the disclosure;
[0070] FIG. 45 illustrates conceptually an alternative embodiment
for two predictions of how the patient state can transition in a
single month given medication change or a dosage change in
accordance with the disclosure;
[0071] FIG. 46 illustrates conceptually one possible embodiment and
scenario of visualization displaying the patient clinical
trajectory and risks in accordance with the disclosure;
[0072] FIG. 47 illustrates conceptually an evaluation of the
patient and the patient trajectory at week 9 at which point risk
based patient monitoring system determines a probability
distribution function for the state of the patient for each of the
past nine weeks in accordance with the disclosure;
[0073] FIG. 48 shows a follow-up evaluation based on teacher and
parent Vanderbilt diagnosis in accordance with the disclosure;
[0074] FIG. 49 shows consequent evaluation based on all available
measurements--office visit, parent and teacher evaluation, which
establishes high probability for significant improvement in
accordance with the disclosure;
[0075] FIG. 50 shows yet another follow-up at which point it is
established that the patient is most probably stably improved, and
has been stably improved between the two evaluations in accordance
with the disclosure;
[0076] FIG. 51 shows a follow-up evaluation of the patient and the
patient trajectory in the absence of measurements, wherein due to
the lack of recent observation, the uncertainty is increasing in
accordance with the disclosure;
[0077] FIG. 52 shows the state of this uncertainty given a full
patient evaluation (all measurement modalities) in accordance with
the disclosure; and
[0078] FIG. 53 illustrates yet another possible visualization from
the described system output. It shows possible patient state
transitions under changes of treatment plan, e.g., change of
medication in accordance with the disclosure.
DETAILED DESCRIPTION
[0079] Technologies are provided herein for providing risk-based
patient monitoring of individual patients to clinical personnel.
The technologies described herein can be embodied as a monitoring
system for critical care, which combines data from various bedside
monitors, electronic medical records, and other patient specific
information to assess the current and the future risks to the
patient. The technologies can be also embodied as a decision
support system that prompts the user with specific actions
according to a standardized medical plan, when patient specific
risks pass a predefined threshold. Yet another embodiment of the
described technologies is an outpatient monitoring system which
combines patient and family evaluation, together with information
about medication regiments and physician evaluations to produce a
risk profile of the patient, continuously track its clinical
trajectory, and provide decision support to clinicians as regarding
when to schedule a visit or additional tests.
[0080] System Modules And Interaction
[0081] Referring now to the Figures, FIG. 1 illustrates a medical
care risk based monitoring environment 1010 for providing health
providers, such as physicians, nurses, or other medical care
providers, risk-based monitoring in accordance with various
embodiments of the present disclosure. A patient 101 may be coupled
to one or more physiological sensors or bedside monitors 102 that
may monitor various physiological parameters of the patient. These
physiological sensors may include but are not limited to, a blood
oximeter, a blood pressure measurement device, a pulse measurement
device, a glucose measuring device, one or more analyte measuring
devices, an electrocardiogram recording device, amongst others. In
addition, the patient may be administered routine exams and tests
and the data stored in an electronic medical record (EMR) 103. The
electronic medical record 103 may include but is not limited to
stored information such as hemoglobin, arterial and venous oxygen
content, lactic acid, weight, age, sex, ICD-9 code, capillary
refill time, subjective clinician observations, patient
self-evaluations, prescribed medications, medications regiments,
genetics, etc. In addition, the patient 101 may be coupled to one
or more treatment devices 104 that are configured to administer
treatments to the patient. In various embodiments, the treatments
devices 104 may include extracorporeal membrane oxygenator,
ventilator, medication infusion pumps, etc.
[0082] By way of the present disclosure, the patient 101 may be
afforded improved risk-based monitoring over existing methods. A
patient specific risk-based monitoring system, generally referred
to herein as system 100, may be configured to receive patient
related information, including real-time information from bed-side
monitors 102, EMR patient information from electronic medical
record 103, information from treatment devices 104, such as
settings, infusion rates, types of medications, and other patient
related information, which may include the patient's medical
history, previous treatment plans, results from previous and
present lab work, allergy information, predispositions to various
conditions, and any other information that may be deemed relevant
to make an informed assessment of the possible patient conditions
and states, and their associated probabilities. For the sake of
simplicity, the various types of information listed above will
generally be referred to hereinafter as "patient-specific
information". In addition, the system may be configured to utilize
the received information, determine the clinical risks, which then
can be presented to a medical care provider, including but not
limited to a physician, nurse, or other type of clinician.
[0083] The system, in various embodiments, includes one or more of
the following: a processor 111, a memory 112 coupled to the
processor 111, and a network interface 113 configured to enable the
system to communicate with other devices over a network. In
addition, the system may include a risk-based monitoring
application 1020 that may include computer-executable instructions,
which when executed by the processor 111, cause the system to be
able to afford risk based monitoring of the patients, such as the
patient 101.
[0084] The risk based monitoring application 1020 includes, for
example, a data reception module 121, a physiology observer module
122, a clinical trajectory interpreter module 123, and a
visualization and user interaction module 124. In an exemplary
embodiment, the data reception module 121 may be configured to
receive data from bedside monitors 102, electronic medical records
103, treatment devices 104, and any other information that may be
deemed relevant to make an informed assessment regarding the
patient's clinical risks, and any combination thereof of the
preceding elements.
[0085] The physiology observer module 122 utilizes multiple
measurements to estimate probability density functions (PDF) of
internal state variables (ISVs) that describe the components of the
physiology relevant to the patient treatment and condition in
accordance with a predefined physiology model. The ISVs may be
directly observable with noise (as a non-limiting example, heart
rate is a directly observable ISV), hidden (as a non-limiting
example, oxygen delivery (DO.sub.2) defined as the flow of blood
saturated oxygen through the aorta cannot be directly measured and
is thus hidden), or measured intermittently (as a non-limiting
example, hemoglobin concentration as measured from Complete Blood
Count tests is an intermittently observable ISV).
[0086] In one embodiment, instead of assuming that all variables
can be estimated deterministically without error, the physiology
observer module 122 of the present disclosure provides probability
density functions as an output. Additional details related to the
physiology observer module 122 are provided herein.
[0087] The clinical trajectory interpreter module 123 may be
configured, for example, with multiple possible patient states, and
may determine which of those patient states are probable and with
what probability, given the estimated probability density functions
of the internal state variables. A patient state is defined as a
qualitative description of the physiology at a particular point of
a clinical trajectory, which is recognizable by medical practice,
and may have implications to clinical decision-making. Examples of
particular patient states include, but are not limited to,
hypotension with sinus tachycardia, hypoxia with myocardial
depression, compensated circulatory shock, cardiac arrest,
hemorrhage, amongst others. In addition, these patient states may
be specific to a particular medical condition, and the bounds of
each of the patient states may be defined by threshold values of
various physiological variables and data. In various embodiments,
the clinical trajectory interpreter module 123 may determine the
patient conditions under which a patient may be categorized using
any of information gathered from reference materials, information
provided by health care providers, other sources of information.
The reference materials may be stored in a database or other
storage device 130 that is accessible to the risk based monitoring
application 1020 via network interface 113, for example. These
reference materials may include material synthesized from reference
books, medical literature, surveys of experts, physician provided
information, and any other material that may be used as a reference
for providing medical care to patients. In some embodiments, the
clinical trajectory interpreter module 123 may first identify a
patient population that is similar to the subject patient being
monitored. By doing so, the clinical trajectory interpreter module
123 may be able to use relevant historical data based on the
identified patient population to help determine the possible
patient states.
[0088] The clinical trajectory interpreter module 123 is capable of
also determining the probable patient states under which the
patient can be currently categorized, given the estimated
probability density functions of the internal state variables, as
provided by physiology observer module 122. In this way, each of
the possible patient states is assigned a probability value from 0
to 1. The combination of patient states and their probabilities is
defined as the clinical risk to the patient. Additional details
related to the clinical trajectory interpreter module 123 are
provided herein.
[0089] Visualization and user interactions module 124 may be
equipped to take the outputs of the data reception module 121 the
physiology observer module 122, and the clinical trajectory
interpreter module 123 and present them to the clinical personnel.
The visualization and user interactions module 124 may show the
current patient risks, their evolution through time, the
probability density functions of the internal state variables as
functions of time, and other features that are calculated by the
two modules 122 and 123 as by-products and are informative to
medical practice. Additionally, visualization and user interactions
module 124 enables the users to set alarms based on the patient
state probabilities, share those alarms with other users, take
notes related to the patient risks and share those notes with other
users, and browse other elements of the patient medical history.
Additional details related to the visualization and user
interactions module 124 are provided herein.
[0090] Physiology Observer
[0091] FIG. 2 illustrates a basic schematic of the physiology
observer module 122, which utilizes two models of the patient
physiology: a dynamic model 212 and an observation model 221. The
dynamic model 212 captures the relationship arising between the
internal state variables at some time t.sub.k and another close
time t.sub.k+1, thereby enabling modeling of the patient physiology
as a system whose present state has information about the possible
future evolutions of the system. Given the propensity of the
patient physiology to remain at homeostasis through
auto-regulation, there is a clear rational of introducing such
memory in internal state variables that are indicative of the
homeostasis, e.g., oxygen delivery and oxygen consumption.
[0092] The observation model 221 may capture the relationships
between measured physiology variables and other internal state
variables. Examples of such models include: a) the dependence of
the difference between systolic and diastolic arterial blood
pressures (also called pulse pressure) on the stroke volume; b) the
relationship between heart rate, stroke volume, and cardiac output;
c) the relationship between hemoglobin concentration, cardiac
output and oxygen delivery; d) the relationship between the
Vanderbilt Assessment Scale and the clinical state of an attention
deficit and hyperactivity disorder patient; and e) any other
dependence between measurable and therefore observable parameters
and internal state variables.
[0093] The physiology observer module 122 functions as a recursive
filter by employing information from previous measurements to
generate predictions of the internal state variables and the
likelihood of probable future measurements and then comparing them
with the most recently acquired measurements. Specifically the
physiology observer module 122 utilizes the dynamic model 212 in
the predict step or mode 210 and the observation model 221 in the
update step or mode 220. During the prediction mode 210, the
physiology observer module 122 takes the estimated PDFs of ISVs 213
at a current time step t.sub.k and feeds them to the dynamic model
212, which produces predictions of the ISVs 211 for the next time
step t.sub.k+1. The is accomplished using the following
equation:
P(ISVs(t.sub.k+1)|M(t.sub.k))=.intg..sub.ISVs.epsilon.ISVP(ISVs(t.sub.k+-
1|ISVs(t.sub.k))(ISVs(t.sub.k)))dISVs
[0094] where ISVs(t.sub.k)={ISV.sub.1(t.sub.k), ISV.sub.2(t.sub.k),
ISV.sub.3(t.sub.k), . . . ISV.sub.n(t.sub.k)} and M(t.sub.k) is the
set of all measurements up to time t.sub.k. The probability
P(ISVs(t.sub.k+1)|ISVs(t.sub.k)) defines a transition probability
kernel describing the dynamic model 212, which defines how the
estimated PDFs evolve with time. The probabilities
P(ISVs(t.sub.k)|M(t.sub.k)) are provided by the inference engine
222 and are the posterior probabilities of the ISVs given the
measurements acquired at the previous time step. During the update
mode 210 of the physiology observer module 122, the predicted ISVs
211 are compared against the received measurements from data
reception module 121 with the help of the observation model 221,
and as a result the ISVs are updated to reflect the new available
information. The inference engine 222 of module 122 achieves this
update by using the predicted PDFs as a-priori probabilities, which
are updated with the statistics of the measurements to achieve the
posterior probabilities reflecting the current ISVs PDFs estimates
213. The inference engine 222 accomplished the update step 220 with
the following equation which is Bayes' Theorem,
P ( ISVs ( t k + 1 ) | M ( t k + 1 ) ) = P ( m 1 ( t k + 1 ) , m 2
( t k + 1 ) , m n ( t k + 1 ) | ISVs ( t k + 1 ) ) P ( ISVs ( t k +
1 ) | M ( t k ) ) P ( M ( t k + 1 ) ) ##EQU00001##
Where P(m.sub.1(t.sub.k+1), m.sub.2(t.sub.k+1), . . .
m.sub.n(t.sub.k+1)|ISVs(t.sub.k+1)) is the conditional likelihood
kernel provided by the observation model 221 that determines how
likely the currently received measurements are given the currently
predicted ISVs.
[0095] At the initialization time, e.g., t=0, when no current
estimate of ISVs PDFs is available, the physiology observer module
122 may utilize initial estimates 250, which may be derived from an
educated guess of possible values for the ISVs or statistical
analysis of previously collected patient data.
[0096] FIG. 3 illustrates a non-limiting example of models that
enable the physiology observer in accordance with the present
disclosure. While not directly observable, the management of oxygen
delivery, DO2, is an important part of critical care. Therefore,
precise estimation of DO2 can inform improved clinical practice. In
the illustrated example, this estimation is achieved through the
measurements of hemoglobin concentration (Hg), heart rate (HR),
diastolic and systolic arterial blood pressures, and SpO2. The
dynamic model 212 assumes that oxygen delivery is driven by a
feedback process which stabilizes it against stochastic
disturbances. Similarly, hemoglobin concentration is controlled
around the norm value of 15 mg/dL. The observation model 221 takes
into account the relationship between arterial oxygen saturation
SpO2, hemoglobin concentration and arterial oxygen content CaO2,
the dependence of the difference between systolic, ABPs, and
diastolic, ABPd, arterial blood pressures (also called pulse
pressure) on the stroke volume, and the relationship between heart
rate, HR, stroke volume, SV, and cardiac output. The two models are
abstracted as a Dynamic Bayesian Network (DBN), and the physiology
observer module 122 utilizes the DBN to continuously track the
oxygen delivery. A Dynamic Bayesian Network is a systematic way to
represent statistical dependencies in terms of a graph whose
vertices signify variables (observable and unobservable), and whose
edges show causal relationships. Further descriptions of an
exemplary DBN for DO2 estimation can be found in U.S. Provisional
Application No. 61/699,492, filed on Sep. 11, 2012, entitled
SYSTEMS AND METHODS FOR EVALUATING CLINICAL TRAJECTORIES AND
TREATMENT STRATEGIES FOR OUTPATIENT CARE, Attorney Docket No.
44429-00107 PROV, and U.S. Provisional Application No. 61/684,241,
filed on Aug. 17, 2012, entitled SYSTEM AND METHODS FOR PROVIDING
RISK ASSESSMENT IN ASSISTING CLINICIANS WITH EFFICIENT AND
EFFECTIVE BLOOD MANAGEMENT, Attorney Docket No. 44429-00106 PROV,
to which priority is claimed, the disclosure of which is
incorporated herein by reference.
[0097] FIG. 4 depicts a non-limiting example of the physiology
observer described above tracking DO2, but over a longer time
interval, i.e., 4 time steps. In the observer, the main hidden ISV
is the oxygen delivery variable (DO2). The two types of
measurements, Hemoglobin (Hg) and oximetry (SpO2) are in dashed
circles in FIG. 4. SpO2 is an example of the continuous or periodic
measurements that the physiology observer module 122 receives from
sensors, such as bedside monitors 102 and treatment devices 104
connected to the patient 101 that continuously report information.
Hemoglobin (Hg) is an example of an intermittent or aperiodic
measurement extracted from patient lab work that is available to
the observer on a sporadic and irregular basis, and latent at
times, relative to current system time. The physiology observer
module 122 is capable of handling both types of measurements
because, along with tracking the hidden ISVs, e.g. DO2, module 122
also continuously maintains estimates of the observed values for
all types of measurements, even when measurements are not present.
FIG. 4 depicts these estimates for the case of SpO2 and Hg. As can
be seen, the SpO2 measurements are available regularly at each time
step, whereas Hg is only available at two of the time steps.
[0098] As mentioned above, certain measurements, such as
Hemoglobin, are available to the system with an unknown amount of
time latency, meaning the measurements are valid in the past
relative to the current time and the time they arrive over the data
communication links. The physiology observer module 122 may handle
such out of sequence measurements using back propagation, in which
the current estimates of the ISVs are projected back in time to the
time of validity of the measurements, so that the information from
the latent measurement can be incorporated correctly. FIG. 5
depicts such time line. In FIG. 5, hemoglobin arrives at the
current system time, T.sub.k, but is valid and associated back to
the ISV (DO2) at time T.sub.k-2. Back propagation is the method of
updating the current ISVs probability estimates
P(ISVs(t.sub.k)|M(t.sub.k)) with a measurement that is latent
relative to the current time, m(t.sub.k-n) Back propagation is
accomplished in a similar manner to the prediction method described
previously. There is a transition probability kernel,
P(ISVs(t.sub.k-n)|ISVs(t.sub.k)), that defines how the current
probabilities evolve backwards in time. This can then be used to
compute probabilities of the ISVs at time t.sub.k-n given the
current set of measurements which excludes the latent measurement,
as follows:
P(ISVs(t.sub.k-n)|M(t.sub.k))=.intg..sub.ISVs.epsilon.ISVP(ISVs(t.sub.k--
n)|ISVs(t.sub.k))P(ISVs(t.sub.k)|M(t.sub.k))dISVs
Once these probabilities are computed, the latent measurement
information is incorporated using Bayes' rule in the standard
update:
P ( ISVs ( t k - n ) | M ( t k ) , m ( t k - n ) ) = P ( m ( t k -
n ) | ISVs ( t k - n ) ) P ( ISVs ( t k - n ) | M ( t k ) P ( M ( t
k ) , m ( t k - n ) ) ##EQU00002##
[0099] The updated probabilities are then propagated back to the
current time t.sub.k using the prediction step described earlier.
Back propagation can be used to incorporate the information.
[0100] Another functionality of the physiology observer module 122
includes smoothing. The care provider using the system 100 may be
interested in the patient state at some past time. With smoothing,
the physiology observer module 122 may provide a more accurate
estimate of the patient ISVs at that time in the past by
incorporating all of the new measurements that the system has
received since that time, consequently providing a better estimate
than the original filtered estimate of the overall patient state at
that time to the user, computing P(ISVs(t.sub.k-n)|M(t.sub.k)).
This is accomplished using the first step of back propagation in
which the probability estimates at time t.sub.k which incorporate
all measurements up to that time are evolved backwards to the time
of interest t.sub.k-n using the defined transition probability
kernel. This is also depicted in FIG. 5, in which the user is
interested in the patient state at T.sub.k-n and the estimates are
smoothed back to that time.
[0101] Because physiology observer module 122 maintains estimates
of each of the measurements available to the system 100 based on
physiologic and statistical models, module 122 may filter artifacts
of the measurements that are unrelated to the actual information
contained in the measurements. This is performed by comparing the
newly acquired measurements with the predicted likelihoods of
probable measurements given the previous measurements. If the new
measurements are considered highly unlikely by the model, they are
not incorporated in the estimation. The process of comparing the
measurements with their predicted likelihoods effectively filters
artifacts and reduces noise. FIG. 6 shows an example of such a
process involving mean arterial blood pressure (ABPm). Because ABPm
is collected using an intravenous catheter, the measured signals
are often corrupted with artifacts that result in incorrect
measurements when the catheter is used for medical procedures such
as blood draws or line flushes. FIG. 6 shows the raw ABPm
measurements prior to being processed by the physiology observer
with the measurement artifacts identified, as well as the filtered
measurements after being processed by the physiology observer
module 122. As can be seen, the measurement artifacts have been
removed and the true signal is left.
[0102] In various embodiments, physiology observer module 122 may
utilize a number of algorithms for estimation or inference.
Depending on the physiology model used, the physiology observer
module 122 may use exact inference schemes, such as the Junction
Tree algorithm, or approximate inference schemes using Monte Carlo
sampling such as a particle filter, or a Gaussian approximation
algorithms such as a Kalman Filter or any of its variants.
[0103] As discussed, the physiology model used by physiology
observer module 122 may be implemented using a probabilistic
framework known as a Dynamic Bayesian Network, which graphically
captures the causal and probabilistic relationship between the ISVs
of the system, both at a single instance of time and over time.
Because of the flexibility this type of model representation
affords, the physiology observer module 122 may utilize a number of
different inference algorithms. The choice of algorithm is
dependent on the specifics of the physiology model used, the
accuracy of the inference required by the application, and the
computational resources available to the system. Used in this case,
accuracy refers to whether or not an exact or approximate inference
scheme is used. If the physiology observer model is of limited
complexity, then an exact inference algorithm may be feasible to
use. In other cases, for more complex physiology observer models,
no closed form inference solution exists, or if one does exist, it
is not computationally tractable given the available resources. In
this case, an approximate inference scheme may be used.
[0104] The simplest case in which exact inference may be used, is
when all of the ISVs in the physiology model are continuous
variables, and relationships between the ISVs in the model are
restricted to linear Gaussian relationships. In this case, a
standard Kalman Filter algorithm can be used to perform the
inference. With such algorithm, the probability density function
over the ISVs is a multivariate Gaussian distribution and is
represented with a mean and covariance matrix.
[0105] When all of the ISV's in the model are discrete variables,
and the structure of the graph is restricted to a chain or tree,
the physiology observer module 122 may use either a
Forward-backward algorithm, or a Belief Propagation algorithm for
inference, respectively. The Junction Tree algorithm is a
generalization of these two algorithms that can be used regardless
of the underlying graph structure, and so the physiology observer
module 122 may also use this algorithm for inference. Junction Tree
algorithm comes with additional computational costs that may not be
acceptable for the application. In the case of discrete variables,
the probability distribution functions can be represented in a
tabular form. It should be noted that in the case where the model
consists of only continuous variables with linear Gaussian
relationships, these algorithms may also be used for inference, but
since it can be shown that in this case these algorithms are
equivalent to the Kalman Filter, the |Kalman Filter is used |[hc1]
the example algorithm.
[0106] When the physiology model consists of both continuous and
discrete ISVs with nonlinear relationships between the variables,
no exact inference solution is possible. In this case, the
physiology observer module 122 may use an approximate inference
scheme that relies on sampling techniques. The simplest version of
this type of algorithm is a Particle Filter algorithm, which uses
Sequential Importance Sampling. Markov Chain Monte Carlo (MCMC)
Sampling methods may also be used for more efficient sampling.
Given complex and non-linear physiologic relationships, this type
of approximate inference scheme affords the most flexibility. A
person reasonably skilled in the relevant arts will recognize that
the model and the inference schemes employed by the physiology
observer module may be any combination of the above described or
include other equivalent modeling and inference techniques.
[0107] When using particle filtering methods, a resampling scheme
is necessary to avoid particle degeneracy. The physiology observer
may utilize an adaptive resampling scheme. As described in detail
below, regions of the ISV state space may be associated with
different patient states, and different levels of hazard to the
patient. The higher the number, the more hazardous that particular
condition is to the patient's health. In order to ensure accurate
estimation of the probability of a particular patient condition, it
may be necessary to have sufficient number of sampled particles in
the region. It may be most important to maintain accurate estimates
of the probability of regions with high hazard level and so the
adaptive resampling approach guarantees sufficient particles will
be sampled in high hazard regions of the state space. FIG. 7
illustrates an example of this resampling. State 1 and State 2 have
the highest hazard level. The left plot depicts the samples
generated from the standard resampling. Notice there are naturally
more particles in state 1 and state 2 region because these states
are most probable. The right plot shows the impact of the adaptive
resampling. Notice how the number of samples in the areas of
highest risk has increased significantly.
[0108] Clinical Trajectory Interpreter
[0109] Referring now to FIG. 8, the Clinical Trajectory Interpreter
123 takes the joint Probability Density Functions of the ISVs from
physiology observer module 122, and performs state probability
estimation 801 to calculate the probabilities of different patient
states. The Probability Density Functions of the ISVs may be
defined in closed form, for example multidimensional Gaussians 260,
or approximated by histogram 280 of particles 270, as illustrated
in FIGS. 2B-D. In both cases, the probability density functions of
the ISVs can be referred to as: P(ISV1(t), ISV2(t), . . . ,
ISVn(t)), where t is the time they refer to. Given the internal
state variables the patient state may be defined by a conditional
probability density function:
P(S|ISV1,SV2, . . . ,ISVn), where S.epsilon.S.sub.1,S.sub.2, . . .
,S.sub.N represents all possible patient states S.sub.i
[0110] Then determining the probability of the patient being in a
particular state S.sub.i may be performed by the equation:
P(S.sub.i(t))=.intg..sub.-.infin..sup..infin. . . .
.intg..sub.-.infin..sup..infin.P(S|ISV.sub.1,ISV.sub.2, . . . ,
ISV.sub.n)P(ISV.sub.1(t),ISV.sub.2(t), . . .
,ISV.sub.n(t))dISV.sub.1 . . . dISV.sub.n
[0111] In case that P(ISV1(t), ISV2(t), . . . , ISVn(t)) is defined
by a closed form function such as multidimensional Gaussian 260,
the integration may be performed directly. In case that P(ISV1(t),
ISV2(t), . . . , ISVn(t) is approximated by a histogram 280 of
particles 270 and P(S|ISV.sub.1, ISV.sub.2, . . . , ISV.sub.n) is
defined by a partition of the space spanned by ISV.sub.1,
ISV.sub.2, . . . , ISV.sub.n into regions as shown in FIG. 9, the
probability P(S.sub.i(t)) may be calculated by calculating the
fraction of particles 270 in each region.
[0112] Once patient state probabilities are estimated, the clinical
trajectory interpreter module 123 may assign different hazard
levels 802 for each patient state or organize the states into
different etiologies 803. The clinical trajectory interpreter
module 123, in conjunction with the physiology observer module 122,
may perform measurements utility determination 804 to determine the
utility of different invasive measurements such as invasive blood
pressures or invasive oxygen saturation monitoring. In one
embodiment, the Clinical trajectory interpreter Module 123
determines the probabilities that the patient is in a particular
state, rather than the exact state that the patient is in.
[0113] FIG. 9 illustrates a non-limiting example of a definition of
a patient state that may be employed by the clinical trajectory
interpreter module 123. Specifically, it assumes that the function
P(S|ISV.sub.1, ISV.sub.2, . . . , ISV.sub.n) may be defined by
partitioning the domain spanned by the internal state variables
ISV.sub.1, ISV.sub.2, . . . ISV.sub.n. The particular example
assumes that the patient physiology is described by two internal
state variables: Pulmonary Vascular Resistance (PVR) and Cardiac
Output (CO). The particular risks and respective etiologies that
may be captured by these two ISVs emanate from the effects of
increased pulmonary vascular resistance on the circulation.
Specifically, high PVR may cause right-heart failure and
consequently reduced cardiac output. Therefore, PVR can be used to
define the attributes of Normal PVR and High PVR, and CO to define
the attributes of Normal CO and Low CO, by assigning thresholds
with the two variables. By combining these attributed, four
separate states can be defined: State 1: Low CO, Normal PVR; State
2: Low CO, High PVR; State 3: Normal CO, High PVR; State 4: Normal
CO Normal PVR.
[0114] FIG. 10 illustrates a non-limiting example of how the
clinical trajectory interpreter module 123 may employ the
definition of patient states to assign probabilities that the
patient may be classified under each of the four possible patient
states at a particular point of time. In the example, the clinical
trajectory interpreter module 123 takes the joint probability
density function of P(Cardiac Output (Tk), Pulmonary Vascular
Resistance (Tk)) and integrates it over the regions corresponding
to each particular state, which produces P(S1(Tk)), P(S2(Tk)),
P(S3(Tk)), and P(S4(Tk)). In this way, the clinical trajectory
interpreter module 123 assigns a probability that a particular
patient state is ongoing, given the information provided by the
physiology observer module 122. Note that if the output of the
physiology observer module 122 is not a closed form function 260
but a histogram 280 of particles 270, the clinical interpreter will
not perform integration but just calculate the relative fraction of
particles 270 within each region.
[0115] FIG. 11 illustrates an alternative approach of estimating
the probabilities for different patient states. In this alternative
approach, to calculate the probabilities P(S1), P(S2), P(S3) and
P(S4), the clinical trajectory interpreter module 123 employs the
joint probability functions of the ISVs for two consecutive time
windows T.sub.k and T.sub.k+1 to calculate a moving window average.
Note in the example that the size of the window is doubled for two
time instances, which indicates that the window may be of an
arbitrary, suitable size. As a result of this moving window
averaging, the clinical trajectory interpreter module 123 performs
a dynamic analysis of the trajectory of the ISVs. That is, it gives
a metric of the probability that the physiology trajectory, as
described by the ISVs, may be found in a particular region in a
particular time frame. In other words, this probability calculation
gives an estimate of the probability that a particular patient
state may be ongoing in the chosen time-frame, as opposed to just
at a chosen time instance.
[0116] Clinical trajectory interpreter module 123 may also assign
hazard levels to each particular state. FIG. 12 illustrates a
non-limiting example of a definition of patient states assigned
with hazard levels by the clinical trajectory interpreter module
123. The hazard levels may be informed from clinician surveys,
reference literature or any other clinical sources. In the
particular example, the clinical trajectory interpreter module 123
distinguishes between four different hazard levels: 1--Minimal
risk, 2--Mild risk, 3--Medium risk, and 4--Severe risk. The
combination of the probability of a patient state and its hazard
level will be referred from hereon as a "Patient risk."
[0117] FIG. 13 illustrates how the patient states and their
respective probabilities may be organized into tree graphs called
etiologies. In particular, the attributes normal and low associated
with the cardiac output ISV are the base nodes of the graph. Each
of these vertexes has two children associated with the attributes
of the pulmonary vascular resistance. This organization leads to
each patient state being a leaf (end vertex) on the tree. This
particular tree will be referred to as an etiology tree. The
etiology tree may be further employed by the visualization and user
interaction module 124 to provide a layered view of the various
patient risks as further described herein.
[0118] FIG. 14 illustrates that the etiology tree may not be unique
for a given set of patient states and physiologic variables.
Specifically, FIG. 14 provides an alternative etiology tree for the
example from FIG. 13. The root of the alternative etiology tree
starts from the attributes associated with the pulmonary vascular
resistance, instead of the attributes associated with cardiac
output. It can be appreciated that different rules may be employed
for generating the trees depending on various factors and the
context of use. For example, one etiology tree may be preferred
against another realization in different clinical situations or
depending on the preference of the users. Moreover, the tree may
dynamically change as the risks change and the clinical situation
evolves.
[0119] Utility of Different Measurements
[0120] During hospital care, there exist measurements that may harm
the patient or slow down their recovery. Examples of such harmful
measurements are all measurements coming from catheters such as
invasive blood pressures and blood oximetry, which have been shown
to significantly increase the risk of infection. Therefore, it may
be useful if, during the care process, the clinician is provided
with an assessment of the utility of each of the potentially
harmful measurements. FIG. 15 illustrates a method for calculating
the utility of different measurements.
[0121] Referring to FIG. 15, the risk-based system 100 and the
clinical trajectory interpreter module 123 may calculate the
utility of a particular measurement with the illustrated procedure.
Particularly, in step 9001, a measurement m.sub.i may be selected.
Given measurement m.sub.i and a current time (t.sub.current), in
step 9002, the clinical trajectory interpreter module 123 may
submit an instruction to the physiology observer module 122 to
simulate the physiology observer module output (the probability
density functions of the internal state variables) from a given
arbitrary point back from the current time (t.sub.current-T) to the
current time t.sub.current with removal of measurement m.sub.i from
the algorithm output. Then, in step 9003, the clinical trajectory
interpreter module 123 may simulate the state probabilities
estimation given the simulated output of the physiology observer
and arrive with a set of patient state probabilities, i.e.,
P.sub.sim(S.sub.1(t.sub.current)),
P.sub.sim(S.sub.2(t.sub.current)), . . . ,
P.sub.sim(S.sub.n(t.sub.current)). Then, in step 9004, using the
state probabilities determined from all available measurements,
i.e., P(S.sub.1(t.sub.current)), (S.sub.2(t.sub.current)), . . . ,
P(S.sub.n(t.sub.current)), the clinical trajectory interpreter
module 123 may calculate the utility of the measurement m.sub.i
using the formula:
U ( m i ) = D ( P sim | P ) = i = 1 n P ( S i ( t current ) ) log (
P ( S i ( t current ) ) P sim ( S i ( t current ) ) ) ,
##EQU00003##
which is also the Kullback-Leibler divergence between the patient
state distribution given all available measurements and the patient
state distribution given the measurement m.sub.i has been removed
for a time interval T.
[0122] Alternatively, in step 9005, the clinical trajectory
interpreter module 123 may calculate utility for m.sub.i by
employing the hazard levels, r.sub.i, assigned to each state
S.sub.i by the formula:
U ( m i ) = D weighted ( P sim | P ) = i = 1 n r i P ( S i ( t
current ) ) log ( P ( S i ( t current ) ) P sim ( S i ( t current )
) ) . ##EQU00004##
In a similar manner, the clinical trajectory interpreter module 123
can perform the utility calculation not only for a particular
measurement, but also for any group of measurements. The utility
calculation can also include a component that captures the
potential harm associated with a particular measurement. For
example, the invasive catheter measurement described above would
have a large level of harm associated with it. In this way, the
calculation trades the harm associated with the measurement against
the value of information it provides. An example of this modified
utility calculation is given by the following formula:
U(m.sub.i)=D.sub.weighted(P.sub.sim|P)-H(m.sub.i),
where H(m.sub.i) defines a function that describes the harm of each
available measure.
[0123] The risk-based monitoring system 100 can also integrate
external computation generated from third party algorithms
implemented either on the same computation medium as the
patient-based monitoring system or as a part of an external device.
FIG. 16 illustrates one possible realization of integration of an
external computation generated from third party algorithms.
Particularly, the output from the external computation 9110 is
provided to the clinical trajectory interpreter module 123 which
implements integration instructions 9120. As a result the state
probability estimation 801 produces new states P(newS.sub.1),
P(newS.sub.2), P(newS.sub.3), . . . , P(newS.sub.n+m), which may
result in an increased number of states n+m from the original
number of n states. Similarly, integration instructions 9120 may be
provided to the hazard level assignment 802 and the etiology
organization 803.
[0124] FIG. 17 illustrates an example of integration instructions.
In the example, it is assumed that the external computation, EC,
provides information about particular binary attributes A=a.sub.1
or A=a.sub.2, and the specific of how the provided information is
captured in the integration instructions by the conditional
probability P(EC|A). Also, given four original states S.sub.1,
S.sub.2, S.sub.3, and S.sub.4, the integration instruction may
specify how the states S.sub.3 and S.sub.4 may be updated with two
additional attributes A=a.sub.1 and A=a.sub.2, and turn into four
new states newS.sub.3, newS.sub.4 newS.sub.5, and newS.sub.6. To
perform this update, the integration instruction may also employ
prior probabilities P(A|S.sub.3) and P(A|S.sub.4). These prior
probabilities may be derived from retrospective studies by
analyzing what fractions of patients exhibiting state S.sub.3 or
S.sub.4 have concomitantly exhibited A=a.sub.1 or A=a.sub.2.
[0125] Another way to derive the prior probabilities is by
soliciting the opinion of clinicians.
[0126] By utilizing the integration instructions, the state
probabilities estimation 801 of the new states may then be derived
from the formula:
P(A=a.sub.j,S.sub.i|EC)=P(EC|A=a.sub.j)P(A=a.sub.j|S.sub.i)P(S.sub.j)/P(-
EC),
where i in {3,4} and j in {1,2}, and where P(S.sub.1) are the
original patient state probabilities derived from the output of the
physiology observer module 122.
[0127] FIG. 18 illustrates an additional example of integration
instructions of an external computation. Again, the risk-based
monitoring system 100 can perform the integration, as shown in FIG.
18, both in the case that the external computation 9110 is
generated on the same computational medium as the patient-based
monitoring system, or as a part of an external device. In this
case, it is assumed that the external computation 9110 provides
direct information about a particular internal state variable
estimated by the physiology observer module 122 (or enhanced
physiology observer module 9300). Therefore, to integrate the
external computation, the physiology observer module 122 can treat
the external computation 9110 as an additional measurement and
integrate it directly into the observation model 221.
[0128] |Visualization and User Interaction|[DB2]
[0129] FIG. 19 illustrates example functionalities of the
visualization and user interactions module 124. Specifically,
module 124 may receive all available patient information and data
including, the data from the data reception module 121, the joint
probability density function produced by the physiology observer
module 122, and the etiology tree, the risks, and the invasive
measurements utilities estimated by the clinical trajectory
interpreter module 123. By utilizing this information, the
visualization and user interactions module 124 may produce: 1) a
unit view 1501 of patients describing their risks, diagnoses, etc.;
2) a view 1502 of a patient's electronic medical record including
laboratory results, prescribed medication, diagnoses, etc.; 3) a
view 1503 of a patient's ongoing risks; 4) a view 1504 of a
patient's risk trajectory, i.e., how the probabilities for
particular patient states have evolved in a particular time frame;
5) a view 1505 of a patient's measurements utilities in the
estimation of the particular patient risks; 6) a plot of a patient
estimated ISVs' PDFs 1506 describing the time evolution of the
ISVs' PDFs; 7) a view 1507 enabling to navigate through the
etiology tree of the patient and thus visualize different levels of
the tree; 8) a view 1508 showing a patient's predicted risks; 9) a
view 1509 enabling clinicians to view and set patient risk based
alarms; 10) a view 1510 of a patient's physiology monitoring data
and its evolution against time; and 11) any combination of the
above described. In addition, the visualization and user
interactions module 124 may also produce a patient tags view 1511,
a patient condition summary 1512, reference and training material
1513, and annotation and tag setting 1514.
[0130] FIG. 20 illustrates an example of a summary view 2000 that
may convey in a single screen a risk profile for each patient in a
particular hospital unit. The risk profile represents what is the
cumulative probability of the patient being in a particular hazard
level. It is calculated by summing the current probabilities of all
states at particular hazard level. In the example, the summed
probabilities, hazard levels are represented by the height of four
bars, each bar corresponding to a particular hazard level. In this
specific example, these hazard levels may be Green (slanted
hatching)--Minimal risk, Yellow (vertical hatching)--Mild risk,
Orange (horizontal hatching)--Medium risk, and Red (dotted
hatching)--Severe risk.
[0131] FIG. 21 illustrates one possible realization of a view 2100
describing the ongoing risks of the patient. Each round-cornered
box corresponds to a particular risk: the color corresponds to the
hazard level with Green (slanted hatching)--Minimal risk, Yellow
(vertical hatching)--Mild risk, Orange (horizontal
hatching)--Medium risk, and Red (dotted hatching)--Severe risk; the
height of the box corresponds to the probability of the particular
patient state. Risks are grouped in columns based on their hazard
levels. The screen and the respective risks are updated in
real-time as new data becomes available.
[0132] Still referring to FIG. 21, in addition to the visualization
of the ongoing patient risks, the system 100 may provide
information about the utility of the various invasive measurements
in determining these risks. Specifically, the illustrated example
gives the utilities of invasive arterial blood pressure (ABP) and
invasive central venous pressure (CVP) measurements. The utility
may be represented by filled bars 2110 and 2120, and the maximum
utility may correspond to six filled bars. The six filled bars may
be displayed in color gradient from 1-dark green, 2-light green,
3-yellow, 4-red, 5-purple, to 6-white or empty. In this particular
embodiment, the filled bars 2110 for ABP show all six colors, while
two of the filled bars 2120 for CVP respectfully show 1-dark green
and 2-light green and the remaining filled bars 2110 show 6-white
or empty.
[0133] FIG. 22 illustrates a view 2200 and how a slider 2210 or
other graphic element on top of the patient view may be utilized in
reviewing the history of the patient risks. Specifically, in the
example, the slider 2210 is moved to show the patient risks at
approximately four hours back from current time. This enables
clinicians to review the continuous evolution of the patient risks
and compare them with the applied treatment or any other external
factors. In various embodiments, slider 2210 may be moved on the
user interface with a pointing device, a command, or, if utilized
in conjunction with touch sensitive displays, through touching and
dragging the slider or other graphic element to designate the
desired time period.
[0134] Referring now to both FIGS. 21 and 22, the etiology tree is
used to combine the two states State A 2130: Hypoxia with low
cardiac output and State B 2140: hypoxia with low Qp:Qs from FIG.
21 to represent them by a single patient state (Hypoxia) 2220. In
the particular example, this is used to fit the text into the
smaller box of FIG. 22 relative to FIG. 21. The user can navigate
the etiology tree in view 2300 by clicking on the composite patient
state 2220 and viewing its constituent patient states 2130 and
2140, as illustrated in FIG. 23.
[0135] |FIG. 24 illustrates in view 2400 how in the same framework
the user may view the predicted risks for the patient by moving the
slider 2410 ahead of current time. |[DB3]
[0136] FIG. 25 illustrates in view 2500 an interactive dialog box
2510 through which the user may define the conditions to set an
alarm for a particular risk. The user achieves this by selecting
the particular risk and then setting upper and lower thresholds for
the patient state probability associated with this risk. No alarm
is activated as long as the patient state probability is between
the upper and the lower threshold. The alarm is activated when the
patient state probability crosses the threshold. Once the alarm is
activated the system 100 may notify a list of chosen people, or
send the notification to another clinical system. Any of module
122-124 may actually store their respective threshold data ranges
and initiate the trigger depending on the specific parameter.
[0137] FIG. 26 illustrates in view 2600 yet another possible
visualization of the patient risk trajectory, i.e. the evolution of
the patient states' probabilities associated with particular risks.
The user may choose what time series of patient state probabilities
he/she wants to display, and the system plots these probabilities
against time.
[0138] FIG. 27 illustrates in view 2700 how the system 100 may
directly present the probability density functions of various
internal state variables. Specifically, in the example, the
estimated PDF of oxygen delivery is plotted in graph 2710 as a
function of time, with darker colors corresponding to higher
likelihood. Similarly, the estimated PDF of mixed venous
oxygenation saturation (SvO2) is plotted in graph 2720 and compared
with actual measurements (dark circles).
[0139] Information Sharing Among Users
[0140] FIG. 28 in view 2800 illustrates an example of a tagging
definition interface 2810 that enables clinicians to mark specific
instances of time or specific periods of time 2820 that are of
interest or represent important points in the clinical course, i.e.
a tag. Tags may be shared or sent via dialog box 2830 to specified
recipients, or may be included in notes or any other part of the
user interface. Users may be able to annotate a tag with particular
comments or observations via dialog box 2840, and tags may be
classified into categories from menu list 2850, for example, a tag
may represent a change in medication dosing, an intervention, a
note regarding monitors or measuring equipment, etc. Tags and their
respective time-series markings may be color coded to indicate
various properties, such as their category. For example, green
tag-marks on a time series may represent changes in medication, red
tag-marks may represent interventions, and yellow tag marks may
represent periods of heightened concern. When setting a tag, the
user may be prompted to define time instance or the time period,
the category of the tag, the annotation for the tag, and how the
tag should be handled by the system. Furthermore, annotations may
be suggested by using natural language processing to convert the
etiologies of the condition into note form.
[0141] FIG. 29 illustrates in view 2900 a Newsfeed view 2910
comprising tags, notes, or information taken from external sources,
such as the time of a blood draw as taken from an electronic
medical record (EMR). The Newsfeed 2910 may allow clinicians to
view and post events, periods of interest, interventions, notes,
tags, etc., which are posted by other clinicians. Clinicians may
view the entire Newsfeed, or sort it based on Tag category, hazard
level, etc. Further, clinicians may search for tags based on
keywords, intervention type, time of stay, source of information,
etc. Entries 2920-2926 on Newsfeed 2010 may indicate any of the
category, source of tag, and patient overview, either in words, or
as a picture, such as the risk profile.
[0142] FIG. 30 illustrates in view 3000 a Condition Summary View
3010 through which the clinician may request a condition summary by
selecting or clicking a particular patient state. The Condition
Summary View 3010 then may present clinicians with a description of
a particular state, including both definitions in window 3020 of
the state, and information regarding how the system arrived at the
conclusion about this patient state probability. This view 3010 may
provide the likelihood and hazard level of the patient state, the
definition of the patient state in terms of ISV thresholds, and the
likelihood of each attribute defining the state, as illustrated and
can also provide a natural language description and evidence window
3030 of evidence contributing to the patient state, by translating
the ISVs PDFs into a qualitative textual description, or by
directly presenting numerical information regarding the evidence.
As an example, FIG. 30 illustrates the Condition Summary View 3010
for the patient state shock due to low cardiac output. Here, the
Condition Summary View 3010 presents the probability of the "shock
due to low cardiac output" state, and the hazard level of the state
shown in color or dotted hatching. Further, the definitions of
shock (mixed venous saturation below or equal to 45%), and low
cardiac output (cardiac output below 3.2 liters per minute per
meter squared), along with the probabilities that each of these are
satisfied (e.g., 40% for shock and 30% for low CO) is presented.
The evidence window 3030 conveys the information which leads to the
assessment of "Shock due to low CO". In this example, the system
has converted the information regarding the probabilities of the
etiologies into a textual form, specifically that the estimated
probability is mainly driven by the fact that there is sub nominal
pulse pressure (systolic blood pressure minus diastolic blood
pressure) indicating reduced stroke volume.
[0143] FIG. 31 further illustrates in view 3100 the ability for the
user interface to include and display reference material, which may
be accessed through the Internet, or stored within the system 100
or remotely accessible thereby. Selecting the Learn More button
3110 in the Condition Summary View 3010 may bring up a Learn More
View 3120, which shows reference information associated with the
Condition Summary View 3010. Reference information may include
causes, interventions, common comorbidities, anatomy, relevant
publications, etc. Furthermore, this feature may serve as a
training tool to familiarize clinicians with managing the
particular patient population, or treatment strategies.
[0144] HLHS Stage 1 Example
[0145] The following description explains how the disclosed system
100 and techniques can be applied to the modeling of the clinical
course of a specific patient population under intensive
care--post-operatively recovering Hypoplastic Left Heart Syndrome
patients after stage one palliation.
[0146] Hypoplastic Left Heart Syndrome is a congenital heart
defect, which is manifested by an underdeveloped left ventricle and
left atrium. As a result, patients suffering from this condition do
not have separated systemic and pulmonary blood flows, but instead
the right ventricle is responsible for pumping blood to both the
body and the lungs. Therefore, the hemodynamic optimization during
intensive care involves managing the fractions of the blood flow
that pass through the lungs (pulmonary flow Q.sub.p) and the body
(systemic flow Q.sub.s). The optimal hemodynamic state is reached
when, adequate tissue oxygen delivery, DO.sub.2, is achieved for a
pulmonary to systemic blood flow ratio, denoted Q.sub.p/Q.sub.s, of
1. Often, to reach this optimal state, the patient physiology
passes through other less beneficial states, and the correct
identification of these states and the application of a proper
treatment strategy for each one of them define the quality of the
post-operative care.
TABLE-US-00001 TABLE 1 Variable Description Units Type DO.sub.2
Indexed Oxygen Delivery mL O.sub.2/min/m.sup.2 Dynamic VO.sub.2
Indexed Oxygen Consumption mL O.sub.2/min/m.sup.2 Dynamic PVR
Pulmonary Vascular Resistance mmHg/L/min/m.sup.2 Dynamic SVR System
Vascular Resistance mmHg/L/min/m.sup.2 Dynamic .DELTA.PVR Change in
PVR per time step mmHg/L/min/m.sup.2 Dynamic .DELTA.SVR Change in
SVR per time step mmHg/L/min/m.sup.2 Dynamic Hb Hemoglobin g/dL
Dynamic/Observed HR Heart Rate Beats per min Dynamic/Observed
SpvO.sub.2 Pulmonary Venous Oxygen Saturation % Dynamic SaO.sub.2
Arterial Oxygen Saturation % Derived/Observed SvO.sub.2 Systemic
Venous Oxygen Saturation % Derived/Observed SpO.sub.2 Pulmonary
Venous Oxygen Saturation % Observed .eta. Aortic Compliance Dynamic
ABPm Mean Arterial Blood Pressure mmHg Derived/Observed CVP Central
Venous Pressure mmHg Dynamic/Observed LAP Left Atrial Pressure mmHg
Dynamic/Observed RAP Right Atrial Pressure mmHg Dynamic/Observed
.DELTA.P Pulse Pressure mmHg Derived/Observed CO Total Cardiac
Output L/min/m.sup.2 Derived Q.sub.p Pulmonary rate of blood flow
L/min/m.sup.2 Derived Q.sub.s Systemic rate of blood flow
L/min/m.sup.2 Derived Q.sub.p:Q.sub.s Ratio of Q.sub.p to Q.sub.s
Derived .DELTA.Q.sub.p:Q.sub.s Change in Ratio of Qp to Qs per time
Derived step C.sub.DO2 Oxygen Delivery feedback constant --
C.sub.VO2 Oxygen Consumption feedback constant -- C.sub.Hb
Hemoglobin feedback constant -- C.sub.3 Aortic compliance scaling
constant -- C.sub.4 Aortic compliance offset -- C.sub.5 Hemoglobin
oxygen carrying capacity --
[0147] Table 1 lists state variables that may be used in the model
of HLHS physiology after stage 1 palliation, the variable
description, units, and type of variable. A person reasonably
skilled in the relevant arts will recognize that though these
variables encompass circulation, hemodynamic, and the oxygen
exchange components of HLHS physiology, the models can be altered
or enhanced with any additional physiologic components such as
ventilation, metabolism, etc. without altering the premise of the
disclosed invention.
[0148] FIG. 32 depicts a general Dynamic Bayesian Network (DBN)
that may be employed to capture the physiology model of the HLHS
stage 1 palliation patients. The graphical model illustrated
conceptually in FIG. 32 captures the causal and probabilistic
relationship between the variables of the model. In the DBN, the
state variables are organized into three groups: dynamic variables
3210, derived variables 3220, and observed variables 3230. Dynamic
variables 3210 are variables whose values change over time based on
a dynamic probabilistic model to be described below. Derived
variables 3230 are quantities that depend on the dynamic variables
with some functional relationship. These variables are computed or
derived when required from the latest dynamic variables and are
therefore also dynamic in nature. Observed variables 3230 are those
variables that are measured directly by one of the sensors
connected to the system and the patient. Observed variables 3230
represent instances of the true dynamic or derived states variables
that have been observed under noise.
[0149] FIG. 33 lists several equations that may be used to model
the dynamics of the HLHS stage 1 physiology. The model consists of
four main types of stochastic models. The first type of model is a
stochastic feedback control model (eqs. 1, 2, and 7). These
variables have a nominal value that the body maintains, but are
disturbed by some random process off of this nominal value. The
strength at which the body attempts to maintain these values is
decided by the feedback constant. The second type of model is a
drift diffusion process (eqs, 3 and 4). These variables are driven
over time by a random white noise process and a drift rate process.
The third type of model is a simple random walk process (eqs. 5, 6,
and 8). The last type of dynamic model is a memory-less process
model in which the variable has no relationship to the variable at
the previous time period, but is simply a random variable which
value changes at each time instance according to some predefined
distribution over the proper support of the variable, i.e. a gamma
distribution over the entire positive real line with parameters A
and B. (eqs. 9, 10, and 11). With the exception of equations 9, 10,
and 11, the driving noise for each dynamic model is independent
white Gaussian noise.
[0150] FIG. 34 depicts example equations that may be used to
abstract the relationships between the dynamic variables in the
model and the derived variables. Some of these functional
relationships are true for general human physiology, but many are
the result of the parallel circulation physiology that is specific
to the HLHS population. Equations 12-15 describe relationships for
variables that are measured directly. Equations 16-18 describe
functional relationships for variables that are of highest interest
when managing care of HLHS patients post-surgery, specifically
Cardiac Output (CO) and Pulmonary to Systemic Flow ratio (Qp:Qs).
These variables cannot be measured directly without complex
procedures.
[0151] Given these functional relationships and the definition of
the dynamic states, FIG. 35 now provides a possible observation
model that may be used to relate the derived variables with the
available sensor data. Each observation model is a conditional
Gaussian relationship. Under this model, the measurement received
from the sensor represents a direct observation of the underlying
state variable corrupted by additional independent Gaussian White
noise with some variance. The figure depicts the observed quantity
as the underlying state variable with a tilde over the variable
name. In this implementation, different sensors can map to the same
underlying state variable, but with potentially different noise
levels. For example, SpO2 as reported by a pulse oximeter measures
the underlying physiology state, SaO2 or arterial oxygen saturation
non-invasively. An intravenous catheter inserted directly into the
arterial blood stream also measures this quantity but in an
invasive way. The catheter measurement should be a more accurate
measurement than the pulse oximetry. In this model, this is handled
with a smaller measurement variance, R.
[0152] In the HLHS physiology observer, inference over the DBN is
performed using a particle filter. As described earlier, a particle
filter is an example of an approximate inference scheme that uses
Monte Carlo samples of the internal state variables to approximate
the probability density function of each state variables with an
empirical distribution based on the number of particles. The filter
uses a process known as Sequential Importance Sampling (SIS) to
continuously resample particles from the most recent approximate
probability distribution. In the filter, each particle is assigned
a weight. When a new observation or measurement arrives, the
weights of each particle are updated based on the likelihood of the
particular particle given the observation. The particles are then
resampled based on their relative updated weights, the particles
with the highest weights being more likely to be resampled than
those with lower weights.
[0153] FIG. 36 illustrates possible attributes, patient states, and
an etiology tree that may be used by the clinical trajectory
interpreter module 123 in the case of the HLHS Stage 1 population.
The variable total cardiac output defined as the sum of the
systemic and the pulmonary blood flows is used to define low and
normal total cardiac output, the Qp:Qs ratio is used to derive low,
balanced, and high Qp:Qs ratio, the value of hemoglobin
concentration Hgb is used to derive low and normal hemoglobin, and
the value of the mixed venous oxygen saturation, SvO2, is used to
derive the attributes hemodynamic shock and no hemodynamic shock.
This results in eight possible states defined as follows: 1) Shock
caused by low total cardiac output, as the presence of both of the
attributes Shock and low total cardiac output; 2) Shock caused by
low hemoglobin as the state with attributes shock, normal total
cardiac output, and low hemoglobin; 3) shock from unknown causes as
the state with the attributes shock, normal total cardiac output,
normal hemoglobin, and balanced circulation; 4) shock cause by low
Qp:Qs as the state with the attributes shock, normal total cardiac
output, normal hemoglobin, and low Qp:Qs; 5) shock cause by high
Qp:Qs as the state with the attributes shock, normal total cardiac
output, normal hemoglobin, and high Qp:Qs; 6) normal circulation as
a state with the attributes of no shock and normal circulation; 7)
low Qp:Qs as the state defined by the attributes of no shock and
low Qp:Qs; and 8) high Qp:Qs as the state defined by the attributes
of no shock and high Qp:Qs. FIG. 35 also illustrates a possible
realization of an etiology tree describing the relationships
between the attributes and the patient states. Using the particles
approximation of internal state variables the probability of the
eight states can be calculated by calculating the relative fraction
of particles within each state.
[0154] Example of Applying The Risk-Based Monitoring System In
Conjunction With Evaluating Consequences Of A Possible
Treatment
[0155] Another possible application of the risk based monitoring
system is to assist clinicians when deciding whether to apply a
particular treatment, one example being blood transfusion.
Transfusion of blood and blood products is a common in-hospital
procedure. Despite that blood transfusion indications and policies
are neither well established nor consistently applied within or
between medical centers. Multiple studies have demonstrated
variation in transfusion practices among different hospitals,
practitioners, and procedures. This variation persists even when
applied to a single procedure (e.g. coronary artery bypass graft
surgery).
[0156] Moreover, blood transfusion has been increasingly recognized
as an independent risk factor for morbidity and mortality. Specific
events and outcomes associated with transfusion include sepsis,
organ ischemia, increased time on ventilation support, increased
hospital length of stay, and short- and long-term morbidity. This
relationship is proportional to the transfusion volume, and
evidence suggests that high hematocrit values may be detrimental.
Understandably, researchers conventionally recommend transfusion
policies aimed at achieving an informed tradeoff between the risks
and benefits.
[0157] Setting robust and effective transfusion policies has been
proven to be a difficult task. The consensus in the medical
community is that simple policies--such as hemoglobin threshold
policies--do not provide adequate guidance. This is due to the
compensatory nature of hemodynamic physiology; patients have a
variable capacity to tolerate low hemoglobin. Consequently,
effective transfusion decision-making must integrate factors such
as compensatory reserve, intravascular volume, hemodynamic
stability, procedure type, and other patient data. Thus, there is
an essential need for blood management policies that will utilize
the full spectrum of relevant clinical variables and determine the
risk/benefit ratio of transfusion. This is exactly afforded by
applying the risk based monitoring system.
[0158] FIG. 37 illustrates one possible environment in which the
risk based monitoring system can be applied to assist clinicians in
deciding whether to apply a particular treatment. Consistent with
the disclosure, a patient 101 is being monitored with multiple
measurements 3910, both intermittently and persistently. The
persistent measurements may include mixed venous oxygen saturation
(SvO2) 3911, systolic, diastolic and mean arterial blood pressures
(ABP s|d|m) 3913, heart rate 3916, monitored via a bedside monitor.
The intermittent measurements may include blood pH 3915, hemoglobin
concentration (Hgb) 3912, and lactic acid concentration 3914
monitored through periodic blood works. These measurements 3910 are
fed into an enhanced risk based monitoring system 3940 with
treatment evaluation, which, in addition to the previously
disclosed physiology observer module 122 and clinical trajectory
interpreter module 123, consists of several other modules. A
possible treatment complications determination module 3924 receives
information from the clinical trajectory interpreter module 123,
together with information about the patient demographics 3931 and
type of procedure 3932. With the information, this module 3924
queries an outcome database 3943 and receives back information of
what the probability of different complications can be given that
a) the patient is in particular patient states with particular
probabilities; b) the patient is of certain demographics (age, sex,
etc); c) the patient has had a particular type of procedure; d) and
any combinations thereof of a) b) and c). On the other hand, the
outcome database 3943 can be populated by using outcome studies
3990 derived from retrospective studies 3991, randomized clinical
trials 3992, institution specific outcomes 3993 determined from
previously collected patient data for a particular institution, and
any combination thereof of the proceeding elements.
[0159] When the possible treatment complications determination
module 3942 determines the possible complications, it feeds this
information back to an enhanced visualization and user interactions
module 3941. The enhanced visualization and user interactions
module 3941 combines the patient-specific risk based monitoring
performed by the physiology observer module 122 and the clinical
trajectory interpreter module 122, with the evaluation of probable
complication. This affords the system to provide a superior vantage
point from which the clinician 3920 can better recognize risks and
benefits of treatments such as blood transfusion, and respectively
more efficiently and effectively decide whether to administer this
treatment 3960 or not.
[0160] FIG. 38 shows a non-limiting example set of patient states
relevant to blood transfusion that may be used to inform the blood
transfusion decision. The states contain information about the
dynamics of the hemoglobin (decreasing/stable/increasing) and the
hemodynamic compensation for reduced blood oxygen carrying
capacity. In uncompensated patients, the hemodynamic
auto-regulation mechanisms become incapable of overcoming the
depleted blood oxygen carrying capacity, marking the onset of
anaerobic metabolism. These seven states can be determined through
three internal state variables: oxygen delivery, hemoglobin, and
rate of hemoglobin production/loss. Specifically, when hemoglobin
is above 13 mg/dL, it is assumed that there is no Hgb related
pathology 4001. When Hgb is lower than 13 mg/dL, there are six
other states, determined through five different attributes. From
the oxygen delivery ISV, the system can determine whether the
patient is compensated or uncompensated, e.g., it may be assumed
that DO2 above 400 ml/min/m.sup.2, for ventilated and paralyzed
patient, indicates compensation, and below this value uncompensated
patient. The other three attributes are determined from the Hgb
rate ISV and are stable 4013 and 4014 (the rate is close to zero),
increasing 4011 and 4012 (the rate is positive), and decreasing
4015 and 4016 (the rate is negative).
[0161] Using The Risk Based Monitoring System With Standardized
Clinical Plan
[0162] Yet another application of the risk based monitoring system
is in applying standardized medical plans. FIG. 39 illustrates one
possible embodiment of this application. Specifically, the data
from the clinical trajectory interpreter module 123 is fed to a
treatment query module 4142. The treatment query module 4142
queries a treatment plan database 4143 based on the determined
patient risks. The treatment plan database 4143 specifies a map
between patient risks and treatments. When the database 4143
returns a treatment plan, it is represented to the clinician 3920
by an enhanced visualization and user interactions module 4141 with
plan. The clinician 3920 can then make clinical decision 4190 with
respect to patient 101. The user decision, the context under which
it was taken, (the calculated patient risks, the estimated ISVs,
and other possible patient data at the time of the decision) are
then recorded to a decision data base 4144. The decision database
4144 then can be compared to patient outcomes and utilized in the
improvement of the treatment plan.
[0163] FIG. 40 illustrates an example application of the risk based
monitoring system 4240 combined with a specific type of
standardized clinical plan. The particular example considers the
medical decision whether to treat the patient with nitric oxide.
Nitric oxide is a pulmonary vasodilator and is used to treat high
pulmonary vascular resistance and ensuing pulmonary hypertension,
which can cause reduced cardiac output. In the example, the medical
plan uses the risks calculated by the clinical trajectory
interpreter module 123 and stratifies 4230 them into two
categories: low risk and high risk. If the risks are low 4201 the
recommended decision is not to treat 4202, respectively, if the
patient is classified as being in high risk the recommended
decision is to treat 4203. The provider can then make a decision to
either follow the recommendations 4250 or disregard them 4260. If
the provider chooses to disregard the treatment recommendation for
a high risk patient, he needs to provide justification 4220.
Likewise, if the provider chooses to treat a low risk patient, he
also needs to provide justification 4210. Justifications 4210 and
4220 in conjunction with patient outcomes may be utilized to refine
the risk stratification 4230 and risk-based monitoring system
100.
[0164] FIG. 41 illustrates the example risk stratification that may
be employed by the system in the context of Nitric Oxide treatment.
Specifically, it assumes that the patient can be in four different
states: State 1: Low CO, Normal PVR; State 2: Low CO, High PVR;
State 1 Normal CO, High PVR; State 4: Normal CO Normal PVR. A
patient being in low risk may be defined as P(State 1)<10% and
P(State 1)+P(State 2)<30%. Similarly high risk may be defined
as: P(State 1)>10% and P(State 1)+P(State 2)>30%.
[0165] Using The Clinical Risk Assessment System In Outpatient Care
Of Chronic Conditions
[0166] Yet another embodiment of the present disclosure allows the
clinical trajectory tracking in outpatient care. Outpatient care of
chronic conditions involves sporadic patient assessment from
intermittent visits, patient self-evaluations, and observations
from caregivers. This leads to uncertainties in determining the
patient clinical course and the efficiency of the prescribed
treatment strategy. To achieve effective patient care management,
clinicians must understand and reduce these uncertainties. They
have two main decisions at their disposal: 1) schedule visits,
prescribe tests, or solicit self-evaluation (or caregiver
evaluations) to improve their understanding of the clinical
trajectory; and/or 2) prescribe changes of medication or medication
dosing to achieve a better trade-off between the likelihood of
improvement and possible side-effects. To inform this decision
making process, there is a need for processing the available
patient information in a way that conveys the clinical trajectory,
the uncertainty in its estimation, and the expected effect that
different treatment strategies may have on the future evolution of
the clinical trajectory.
[0167] As a non-limiting example embodiment of the risk based
monitoring system to outpatient clinical trajectory tracking, we
consider its application to the outpatient care of Attention
Deficit and Hyperactivity Disorder (ADHD) of pediatric patients.
FIG. 42 illustrates possible patient states that may describe the
clinical trajectory of an ADHD patient. They are the same as the
ones used by the Clinical global impression-improvement scale: 1)
very much worse; 2) much worse; 3) worse; 4) No change; 5)
Minimally Improved; 6) Much improved; 7) Very much improved. The
Patient State Distribution (PSD) is the set of probabilities that
the patient is in any of the seven states, given all available
information and observations.
[0168] To evaluate the patient state, a clinician may either
schedule an office visit for direct examination, or may request a
Vanderbilt diagnostic test from family members or teachers (the
test is modified depending on the respondent, teacher or parent).
FIG. 43 lists the available patient evaluation modalities as M1. M2
and M3. Models may be used to map the test questions and answers
into the states of the patient. Both the clinical evaluation and
the test-based evaluation are associated with uncertainty that
prohibits the exact determination in which of the seven states the
patient currently resides.
[0169] The dynamic model or the patient evolution from state to
state may be abstracted by a Dynamic Bayesian Network (DBN) as the
one shown in FIG. 44. In FIG. 44, the arcs' directions signify
statistical dependence, i.e., the connection from "Patient state @
t1" 4601 to "M1", signifies the probability density function (PDF):
P(M1|Patient state @ t1). Similarly, the depicted DBN illustrates
that "Patient state @ t2" 4602 (the patient state at a particular
time t2), is conditioned on the "Patient state @ t1" (the patient
state at the previous time increment t1). In the spirit of the
present disclosure, this model enables the estimation of the
patient state distribution even in the absence of some or all
possible measurements, e.g., as illustrated in the figure at time
instance t2 when M1 is missing, and at time instance t3 ("Patient
state @ t3" 4603), when all measurements are missing.
[0170] FIG. 45 illustrates an alternative embodiment for two
predictions of how the patient state can transition in a single
month given medication change or a dosage change. This prediction
is performed based on a statistical model derived in the following
fashion: Step 1: Isolate a group of patients from retrospective
data which at some point of their treatment have passed through
State A and received a change of treatment (Med1 Dose1->Med 2
Dose 2); Step 2: For each patient, set the time instance that this
particular event occurred to t0; 3) step 3: for each patient
identify what is the patient state at time t0+1 Month (1M) (or any
desired time step unit). 4) calculate the fraction of patients that
transition State A->State i where i stands for all seven
possible patient states; 5) set the fraction as the probabilities
for transition under the particular treatment change.
[0171] FIG. 46 shows one possible embodiment and scenario of
visualization displaying the patient clinical trajectory and risks.
The user interface denotes that the patient is at "no change"
state, and that this has been established by three separate
measurements: office visit, teacher based Vanderbilt diagnosis, and
parent based Vanderbilt diagnosis. The solid line on the screen
signifies that a medication has been prescribed to the patient
(Medication 1) at week 1 of the treatment.
[0172] FIG. 47 shows an evaluation of the patient and the patient
trajectory at week 9 at which point the clinical risk assessment
system determines a probability density function for the state of
the patient for each of the past six weeks. The available
measurements at this point are teacher and parent Vanderbilt
diagnosis. In the illustrated example, due to a high probability of
a deteriorating patient state, the clinician prescribes a change of
medication dosing, which is depicted by a dashed red line.
Additionally, the user interface shows the side effect reported by
the patient--headache.
[0173] FIG. 48 shows a follow-up evaluation based on teacher and
parent Vanderbilt diagnosis. In the example, the clinician decides
a medication change depicted by a hollow line.
[0174] FIG. 49 shows consequent evaluation based on all available
measurements--office visit, parent and teacher evaluation, which
establishes high probability for significant improvement.
[0175] FIG. 50 shows yet another follow-up at which point it is
established that the patient is most probably stably improved, and
has been stably improved between the two evaluations. Note that due
to the applied inference, the PDF for the patient trajectory is
continuously estimated. However, the precision (the concentration
of the PDF) is higher in the presence of a measurement.
[0176] FIG. 51 shows a follow-up evaluation of the patient and the
patient trajectory in the absence of measurements. Due to the lack
of recent observations, the uncertainty is increasing.
[0177] FIG. 52 shows the state of this uncertainty given a full
patient evaluation (all measurement modalities). The inference
engine propagates this uncertainty back in time to produce a more
precise estimation of the patient trajectory, which helps the
clinician to deduct that the patient is stable.
[0178] FIG. 53 illustrates yet another possible visualization from
the described system output. It shows possible patient state
transitions under changes of treatment plan, e.g., change of
medication. It also conveys what possible side-effects can be
expected. For every side effect, there are three stages of
manifestation--mild, moderate, and severe represented with the
three boxes next to each side effect in the figure. The coloring
corresponds to the probability of a particular severity
manifestation for each particular side-effect, with darker colors
indicating higher probability.
[0179] Various examples and embodiments consistent with the present
disclosure have be described in detailed above. It is to be
understood that these examples and embodiments of the present
disclosure are provided for exemplary and illustrative purposes
only. Various modifications and changes may be made to the
disclosed embodiments by persons skilled in the art without
departing from the scope of the present disclosure as defined in
the appended claims.
* * * * *