A MULTI-PHASE ANCHOR-BASED DIAGNOSTIC DECISION-SUPPORT METHOD AND SYSTEM

PROCEDE ET SYSTEME MULTIPHASE DE SOUTIEN A LA DECISION DIAGNOSTIQUE BASES SUR UN POINT D'ANCRAGE

English Abstract

A medical diagnosis decision support system for assisting a health
professional to
diagnose a medical condition. The system is first provided with an anchor
condition
that can be a symptom, a sign, a laboratory test result, or an imaging test
results or
any combination thereof. The system then guides users in a series of
predetermined
phases regarding abstract or concrete diagnosis groups that should be
considered
and appropriate data that should be collected during the clinical
investigation
process. The system suggests history and physical examination clinical data
items,
laboratory, and imaging tests that should be collected in order to
differentiate
among alternative diagnoses. In each phase, possible diagnoses are listed and
ranked.

Claims

CLAIMS

1. A computerized medical diagnosis method for assisting a health professional

to diagnose a medical condition, comprising the steps of:

(i) providing an anchor condition;

(ii) associating with said anchor condition a plurality of diagnoses whose
main
clinical manifestation is said anchor condition;

(iii) associating with each diagnosis of said plurality of diagnoses a weight
that
represents the likelihood that the diagnosis is manifested as the anchor
condition;

(iv) associating a plurality Clinical Data Items (CDIs) with each diagnosis of
said
plurality of diagnoses given an anchor condition, each combination of CDI
and a diagnosis is assigned an evoking strength in which the CDI evokes the
diagnosis and a penalty that the diagnosis receives in the absence of a
finding;

(v) calculating a total weighted score for each possible diagnosis of the
patient
among the plurality of diagnoses by adding for each diagnosis: the likelihood
that the diagnosis is manifested as the anchor, the evoking strengths of each
CDI that is true for the patient and subtracting for each CDI that is absent
in
the patient the penalty weight for defined penalty relationships between the
diagnosis and the CDIs;

(vi) choosing a diagnosis above a cutoff level of the weighted score and
making
it an anchor condition; and

(vii) repeating steps (ii) through (vi) until the diagnosis with the highest
weighted
score, which now serves as an anchor condition, does not have any
associated diagnoses and is thus determined to be the diagnosis for said
medical condition.


44



2. A medical diagnosis method according to claim 1, wherein the anchor
condition is a symptom, a sign, a laboratory test result, an imaging test
results or
any combination thereof.

3. A medical diagnosis method according to claim 1, further comprising the
step of suggesting clinical data items, laboratory, and imaging tests that
should be
collected in order to differentiate among alternative diagnoses.

4. A medical diagnosis method according to claim 1, further comprising the
step of listing and ranking possible diagnoses of said medical condition.

5. A medical diagnosis method according to claim 1, further comprising a
preliminary step of creating a knowledge base for medical conditions and their

associated main clinical manifestations (MCMs).

6. A medical diagnosis method according to claim 1, further comprising the
step of indicating the probability of occurrence of a MCM given a disease.

7. A medical diagnosis method according to claim 1, wherein combinations of
two or more CDIs occurring together or in a certain temporal pattern, favor a
certain
diagnosis over the additive contribution of each one of the CDIs alone.

8. A medical diagnosis method according to claim 1, wherein when the patient
does not exhibit a CDI that is frequent in a disease hypothesis, points are
deducted
from that hypothesis.

9. A medical diagnosis method according to claim 1, wherein in step (vi) a
user
overrides a recommended diagnosis above the cutoff level of the weighted score
and
select instead a different diagnosis and make it an anchor condition.

10. A computerized medical diagnosis method for assisting a health
professional
to diagnose a medical condition assuming findings are conditionally
independent
given any disease hypothesis comprising the steps of:





(i) providing an anchor condition;

(ii) associating with said anchor condition a plurality of diagnoses whose
main
clinical manifestation is said anchor condition;

(iii) arranging the knowledge about a disease in sets of small Bayesian
Networks
(BNs), wherein each BN corresponds to one phase of a disease Knowledge-
Base (KB) and comprises the relevant findings and disease hypotheses for
that phase, the prior probabilities of the disease hypotheses and the
conditional probabilities P(F¦D) representing frequency data;

(iv) moving from the BN of one phase to the BN of the next phase by setting
the
diagnosis with the highest posterior probability from the prior phase as the
anchor of the BN corresponding to the next phase, wherein the prior
probabilities of the diagnoses belonging to the next phase given the anchor
are computed by constructing an intermediate BN comprising of the diseases
or abstractions used in the DD-set of a phase and the phase's anchor
condition such that the probabilities used in said intermediate BN are the
prior probabilities of the diseases, and the conditional probabilities
relating
the anchor condition to the individual disease hypotheses P(anchor¦D) are
derived from the disease_manifested_as_anchor relationships, and the
computed prior probabilities of said intermediate BN serve as the prior
probabilities for the diseases in the BN of the next phase;

(v) repeating step (iv) until the diagnosis with the highest posterior
probability
does not serve as an anchor of a next BN and is thus determined to be the
diagnosis for said medical condition.

11. A computerized medical diagnosis system for assisting a health
professional
to diagnose a medical condition, comprising:

(i) means for providing an anchor condition;

46



(ii) means for associating with said anchor condition a plurality of diagnoses

whose main clinical manifestation is said anchor condition;

(iii) means for associating with each diagnosis of said plurality of diagnoses
a
weight that represents the likelihood that the diagnosis is manifested as the
anchor condition;

(iv) means for associating a plurality Clinical Data Items (CDIs) with each
diagnosis of said plurality of diagnoses for a given anchor condition, each
combination of CDI and a diagnosis is assigned an evoking strength in which
the CDI evokes the diagnosis and a penalty that the diagnosis receives in the
absence of a finding;

(v) means for calculating a total weighted score for each possible diagnosis
of
the patient among the plurality of diagnoses by adding for each diagnosis: the

likelihood that the diagnosis is manifested as the anchor, the evoking
strengths of each CDI that is true for the patient and subtracting for each
CDI
that is absent in the patient the penalty weight for defined penalty
relationships between the diagnosis and the CDIs;

(vi) means for choosing a diagnosis above a cutoff level of the weighted score

and making it an anchor condition; and

(vii) means for repeating steps (ii) through (vi) until the diagnosis with the
highest
weighted score, which now serves as an anchor condition, does not have any
associated diagnoses and is thus determined to be the diagnosis for said
medical condition.

12. A medical diagnosis system according to claim 11, wherein the anchor
condition is a symptom, a sign, a laboratory test result, an imaging test
results or
any combination thereof.


47



13. A medical diagnosis system according to claim 11, wherein clinical data
items, laboratory, and imaging tests suggested that should be collected in
order to
differentiate among alternative diagnoses.

14. A medical diagnosis system according to claim 11, possible diagnoses of
said
medical condition are listed and ranked.

15. A medical diagnosis system according to claim 11, wherein a knowledge
base for medical conditions and their associated main clinical manifestations
is first
created.

16. A medical diagnosis system according to claim 11, wherein the probability
of
occurrence of a MCM given a disease is indicated.

17. A medical diagnosis system according to claim 11, wherein in step (vi) a
user
overrides a recommended diagnosis above the cutoff level of the weighted score
and
select instead a different diagnosis and make it an anchor condition.

18. A computer-readable medium encoded with a program module that executes
a medical diagnosis method for assisting a health professional to diagnose a
medical
condition, by:

(i) providing an anchor condition;

(ii) associating with said anchor condition a plurality of diagnoses whose
main
clinical manifestation is said anchor condition;

(iii) associating with each diagnosis of said plurality of diagnoses a weight
that
represents the likelihood that the diagnosis is manifested as the anchor
condition;

(iv) associating a plurality Clinical Data Items (CDIs) with each diagnosis of
said
plurality of diagnoses given an anchor condition, each combination of CDI
and a diagnosis is assigned an evoking strength in which the CDI evokes the

48



diagnosis and a penalty that the diagnosis receives in the absence of a
finding;

(v) calculating a total weighted score for each possible diagnosis of the
patient
among the plurality of diagnoses by adding for each diagnosis: the likelihood
that the diagnosis is manifested as the anchor, the evoking strengths of each
CDI that is true for the patient and subtracting for each CDI that is absent
in
the patient the penalty weight for defined penalty relationships between the
diagnosis and the CDIs;

(vi) choosing the diagnosis above a cutoff level of the weighted score and
making
it an anchor condition; and

(vii) repeating steps (ii) through (vi) until the diagnosis with the highest
weighted
score, which now serves as an anchor condition, does not have any
associated diagnoses and is thus determined to be the diagnosis for said
medical condition.


49

Description

CA 02702720 2010-05-04

A MULTI-PHASE ANCHOR-BASED DIAGNOSTIC DECISION-
SUPPORT METHOD AND SYSTEM
TECHNICAL FIELD
The present invention relates to decision support systems in general, and in
particular to medical diagnosis systems.

BACKGROUND ART
A medical diagnosis process is a complex cognitive process comprising a
variety of different types of problem solving tasks that are involved in the
clinical
reasoning process. In addition, physicians must follow progress in clinical
research

and incorporate ever growing new knowledge regarding diagnosis of clinical
problems and diseases. Clinical Decision Support Systems (DSSs) have been
recognized as important tools to aid clinicians in gathering relevant
knowledge and
data, making clinical decisions, managing medical actions more effectively,
and
thus achieving reduced practice errors, a higher standard of care, and reduced
costs.

Clinical DSSs can provide tools for information management (e.g., retrieval
and
storage), for focusing attention (e.g., alerts and reminders), and for
providing
patient-specific recommendations. Diagnostic DSSs assist a clinician with one
or
more component steps of the clinical diagnostic process.

Currently, relatively few diagnostic DSS are being used and the rate of usage
in routine clinical practice is considered low. Part of the difficulty
experienced in
incorporating them may be associated with the lack of integration into the
clinical
reasoning process involved in clinical diagnosis.

In essentially all of the present diagnostic DSSs, the user enters data about
symptoms, signs, and laboratory test results, and the DSS produces a list of
possible
diagnoses. These DSSs help the user in selecting controlled vocabulary terms
to

describe the findings. Some systems may guide the user with the diagnostic
process,
incorporating rule-in and rule-out diagnostic processes depending on the
scores of
the hypotheses in the Differential- Diagnosis set (DD-set). Other systems may
offer
decision-support services that the user can invoke, such as providing a
disease
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CA 02702720 2010-05-04

decision-support services that the user can invoke, such as providing a
disease
profile, focusing the diagnosis on important features, viewing evidence for a
diagnosis, and obtaining explanations of findings. Most of the existing
diagnostic
DSSs are used by novice clinicians, or by experienced clinicians to aid them
in
diagnosing difficult cases.

Available probabilistic diagnostic DSSs today are not specially tailored
toward assisting expert and non-expert physicians in the proper and efficient
workup of a clinical manifestation which may be a symptom, sign, abnormal
laboratory or imaging test results, or any combination of these.

Developing diagnostic DSSs that cover large domains poses great challenges
including:

(1) Acquiring the clinical knowledge and keeping it up to date. Knowledge
can be acquired by eliciting it from domain experts or it can be gathered from
the
literature or by compiling data found in electronic medical record systems.

(2) Representing and reasoning with the clinical knowledge. The main
decision-support models are quantitative (e.g., statistical models including
Bayesian
Networks, machine learning approaches) or qualitative (e.g., heuristic
knowledge
represented as rules, ontologies, or decision tables).

(3) Supporting the sequence of reasoning used in the diagnosis process.

(4) Integrating with controlled vocabularies and clinical information systems.
(5) Supporting system evolution, including evaluation, testing, and quality
control.
(6) Addressing legal and ethical issues.

Available diagnostic decision-support systems for broad medical domains
The "Leeds abdominal pain system" [de Dombal FT. Computer-aided
diagnosis and decision-making in the acute abdomen. J R Coll Physicians Lond
1975;9(3):211-8] was the first diagnostic DSS, published in 1972. Since then,
a
number of computer-based systems with diagnostic capabilities have been

developed for broad ranges of diseases. Examples include Dxplain [Barnett GO,
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CA 02702720 2010-05-04

AMIA Symp 1998:607-11], Iliad [Lincoln MJ, Turner CW, Haug PJ, Warner HR,
Williamson JW, Bouhaddou 0, et al. Iliad training enhances medical students'
diagnostic skills. J Med Syst 1991;15(1):93-110], Meditel [Waxman HS, Worley
WE. Computer-assisted adult medical diagnosis: subject review and evaluation
of a

new microcomputer-based system. Medicine (Baltimore) 1990;69(3):125-36.],
Quick Medical Reference (QMR) [Miller R, Masarie FE, Myers JD. Quick Medical
Reference (QMR) for diagnostic assistance. MD Comput 1986;3(5):34-48],
Problem Knowledge Coupler (PKC) [Weed LL, Hertzberg RY. The use and
construction of problem-knowledge couplers, the knowledge coupler editor,

knowledge networks, and the problem-oriented medical record for the
microcomputer. Proc Symp Comp Appl Med Care 1983:831-6], Isabel
[Ramnarayan P, Kulkarni G, Tomlinson A, Britto J. ISABEL: a novel Internet
delivered clinical decision support system. In: Bryant J, editor. Current
perspectives
in healthcare computing. Harrogate, UK; 2004. p. 245-54], Physician Assistant

Artificial Intelligence Reference System (PAIRS) [Logic Medical Systems. PAIRS
(Physician Assistant Artificial Intelligence Reference System); 2007.
Available
from: http://cyberdoc.freewebspace.com/, last accessed June 25, 2009]
(previously
known as QMR-DT [Shwe M, Cooper G. An empirical analysis of likelihood-
weighting simulation on a large, multiply-connected medical belief network.

Comput Biomed Res 1991;24(5):453-75]), and Global Infectious Diseases and
Epidemiology Network (GIDEON) [Edberg SC. Global Infectious Diseases and
Epidemiology Network (GIDEON): a world wide Web-based program for diagnosis
and informatics in infectious diseases. Clin Infect Dis 2005;40(1):123-6].
These
systems differ in the data used to determine their probability estimates, the
extent to
which diseases and related clinical data are addressed in their knowledge
bases, the
particular vocabulary they require to describe clinical data, and the
computational
model they use to combine and analyze data, as shown in Table 1 and described
in
detail below.

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Table 1- Characteristics of diagnostic DSS's currently available

QMR DXplain Iliad GIDEON QMR-DT Isabel Problem The DSS of
(PAIRS) Knowledge Coupler the invention
Sources of Literature and Literature Literature Literature Literature
Literature Literature Literature
knowledge experts and and experts
experts

Main Clinical - - - - - - + +
Manifestation
(MCM)-oriented
DD shown at + + + + + + Can be requested +
each stage

Advice on +/- Advice is + + - - - + +
data/tests that not actively Collection
should be offered. Users of text
collected can invoke material
menu option indexed
for ruling-in with the
and ruling-out disease
diagnoses
concepts is
accessible
Representation - - - + - - - +
of temporal
relationships
between data
items

Synergism - - + + + - - +
between data
items
Probabilistic + + + - - +
ranking of the
diseases
Consideration of - - - - - - - +
doctor's clinical
reasoning , in
which clinical
investigation is
done in stages
of decreasing
abstraction
Local adaptation - + some - + - - possible +
of system support

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CA 02702720 2010-05-04

Ability of user + + - - - - + +
to override
system
recommendation
s, selecting a
different DD set

Consideration of + + Rare + + + + Only relevant Only relevant
all hypotheses disease diagnoses are listed diagnoses are
displayed listed
separately

Scope of Diseases and Diseases Diseases Diseases Diseases Diseases Knowledge
relevant Knowledge
knowledge findings for and and and and findings and for MCM relevant for
internal findings findings findings for internal findings for MCM
medicine for for for medicine internal
internal internal Infectious medicine,
medicine medicine diseases pediatrics,
geriatrics
Explanations + what + what + + - - References to +
provided findings findings literature may be
supporta support a provided
diagnosis diagnosis

Computational Disease/finding Disease/fi Bayes + Bayes Bayes Pattern-
Disease/finding Multi phase,
model Relationships riding decision matching Relationships; anchor based,
Relationsh rules algorithms predecessor/success relational or
ips or relations between Bayesian
entities. No ranking
of disease
hypotheses
* Focusing the diagnosis on selected findings is possible

In terms of the computational model, Iliad, Meditel, PAIRS, and GIDEON
are based on Bayes' theorem; for example, the differential diagnosis list in
GIDEON is based on a Bayesian formula that compares the mathematical product
of disease incidence times the rate of symptom occurrence for all relevant
infectious
diseases within a given country. In addition to using Bayes theorem, Iliad
also uses
decision rules for reasoning with clusters of conditionally-independent
findings.
This is meant to solve the problem of over confident, unreliable diagnostic
results
that occurred because findings were not completely independent.

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Isabel uses pattern-matching algorithms to compare findings entered by a
user to terms used in a selected reference library that includes text from
medical
books and journals. By collating text related to one specific diagnosis under
a single
diagnostic label within a pre-designed diagnostic tree, it was possible for
the
software to generate a unique signature of key concepts for each diagnosis.

DXplain and QMR (and QMR's predecessor, Internist-1) use non-Bayesian
algorithms that focus on a relational model describing relationships between
case
findings (symptoms, signs, laboratory data) and individual diseases to derive
a
weighted assessment of a patient's clinical presentation. In Internist-1
[Miller RA,

Pople HE, Myers JD. Internist-1, an experimental computer-based diagnostic
consultant for general internal medicine. N Engl J Med 1982;307(8):468-76] as
well as in QMR and DXplain, one type of disease-finding relationship
represents
the frequency with which the finding occurs in the disease, and the other the
degree
to which the presence of the finding suggests consideration of the disease
(evoking

strength). Other tables store the importance of explaining findings, disease
frequencies (prevalence) and disease importance (impact of not considering the
disease if it is present). The DXplain algorithm also considers the number of
diseases in the Differential-Diagnosis set (DD-set).

Problem Knowledge Coupler (PKC) takes a philosophical stand that the
assesser should understand the pattern of findings (and test results)
occurring for her
patient gather relevant knowledge, in the other systems the knowledge from the
literature is incorporated into the knowledge base manually.
All of the diagnostic DSSs can aid a physician during his clinical reasoning
process. They all allow the user to start the diagnosis process with patient
findings,
but out of the systems surveyed in Table 1, only Iliad, DXplain, and PKC
provide

advice on data that should be collected and laboratory tests that should be
employed. In PKC, the sequence of data that should be collected is represented
in
the system's model ahead of time.

GIDEON is the only system that explicitly represents temporal relationships
between data items. All the systems that use a Bayesian computational model
can
6


CA 02702720 2010-05-04

support synergistic effects between findings, i.e., findings that together
suggest a
diagnosis with a higher probability. GIDEON, and to some extent also DXplain,
consider the geographical location of the patient as a factor in the
diagnostic
process. When findings are entered, all possible diagnoses that cover those
findings

are considered in all of the DSS, but disease prevalence is taken into account
for
ranking the possible diagnoses.

In DXplain, rare diseases are displayed separately. The different diagnostic
DSSs all provide explanations for why each of these diseases might be
considered.
DXplain also lists the clinical manifestations, if any, which would be unusual
or

atypical for each of the specific diseases and GIDEON also explains why other
diagnoses are not considered. In Isabel, the explanations are in the form of
linking
with up to date knowledge from textbooks and journals. However, none of the
diagnostic DSSs currently in use offer pathophysiological reasoning that
create
models of a specific patient's illness.


Hypothetico-deductive reasoning and main clinical manifestation-oriented
diagnosis

Several cognitive models of clinical diagnostic reasoning processes have
been developed. Some of the highly-accepted models view the diagnostic process
as
either hypothesis formulation or pattern recognition [Elstein AS, Schwartz A.

Chapter 10: clinical reasoning in medicine. In: Higgs J, Jones MA, editors.
Clinical
reasoning in the health professions. Butterworth-Heinemann; 2000]. Hypothetico-

deductive reasoning [Shortliffe EH, Barnett GO. Chapter 2: biomedical data:
their
acquisition, storage, and use. In: Shortliffe EH, Cimino JJ, editors.
Biomedical
informatics: computer applications in health care and biomedicine. Springer;
2006]
is an iterative process, which involves staged data collection followed by
data
interpretation and the generation of a set of hypotheses (which in the case of
clinical
diagnosis is known as the DD-set), leading to hypothesis-directed selection of
the
next most appropriate data to be collected. The data collected at each stage
are used

to reformulate or refine the active hypotheses. The reasoning process is
iterated
7


CA 02702720 2010-05-04

until one hypothesis reaches a threshold level of certainty. The staged-
process helps
to focus the reasoning process. When physicians have collected initial data
from the
patients' history and physical examination, they can generate an initial DD-
set. By
that time, physicians have expectations of what they will find on further

examination or may have specific tests in mind that will help them to
distinguish
among still active hypotheses.

A clinical investigation usually starts from some clinical anchor finding.
Many times, this clinical anchor is the reason for patients to seek medical
care as
well as for physicians to initiate an investigation. This anchor is referred
to herein as

a Main Clinical Manifestation (MCM), which may consist of a single clinical
problem such as, diarrhea, syncope, or jaundice, laboratory test result (e.g.,
hyponatermia), or combinations of several linked findings, such as fever and
rash
(which is a common clinical manifestation in pediatrics). The MCM plays an
important role in focusing the diagnostic process. This is in concert with the

findings of Eddy and Clanton [Eddy DM, Clanton CH. The art of diagnosis:
solving
the clinicopathological conference. N Engl J Med 1982;306(21):1263-9] who
showed that identification of a pivotal finding is often used to simplify the
diagnostic problem and to narrow the focus to a limited set of hypotheses.
During
the clinical reasoning process, when doctors consider the various possible
diagnoses

that explain the MCM they take into account the probability of each diagnosis
to be
manifested as the MCM, ranking diagnoses that are more likely to be manifested
as
the MCM higher.

During the diagnostic process, physicians collect and analyze several types
of data types, including subjective information acquired by questioning the
patient
(i.e., symptoms or medical history), objective findings obtained by performing

physical examination (i.e., signs) and all sorts of laboratorial and imaging
data. At
any point in this process, there are several diagnoses that might fit the data
collected
(i.e., differential diagnosis). Their number should decrease as the diagnostic
process
progresses. As has already been shown years ago, expert clinicians can make a

diagnosis in the majority of patients using the history and physical
examination data
8


CA 02702720 2010-05-04

alone [Hampton JR, Harrison MJ, Mitchell JR, Prichard JS, Seymour C. Relative
contributions of history-taking, physical examination, and laboratory
investigation
to diagnosis and management of medical outpatients. Br Med J
1975;31(2(5969)):486-9.; Peterson M, Holbrook JH, Hales DV, Smith NL, Staker

LV. Contributions of the history, physical examination, and laboratory
investigation
in making medical diagnoses. West J Med 1992;156(2):163-5].

MCM-oriented diagnosis is a well-accepted approach in clinical diagnosis. It
is evident in medical books [Behrman RE, Kliegman RM, Jenson HB. Nelson
textbook of pediatrics. 17th ed. Elsevier; 2004; Fauci AS, Braunwald E, Kasper
DL,

Hauser SL. Harrison's principles of internal medicine. 17th ed. McGraw-Hill
Professional; 2008] (e.g., diagnosing fever and rash in children).
Traditionally, text
books often did not report evidence-based (EB) statistics regarding disease
prevalence per clinical problem or frequency of clinical data items given a
disease.
Medical books based on the principles of evidence-based MCM-oriented diagnosis
that report such data are becoming more prevalent.

There is thus a great need for diagnostic DSSs that would support the
investigation process of clinical problems.

SUMMARY OF INVENTION

It is an object of the present invention to provide a medical diagnostic
decision-support system.

It is another object of the present invention to provide a medical diagnostic
decision-support system that is MCM-oriented.

It is a further object of the present invention to provide a medical
diagnostic
decision-support system that is MCM-oriented and supports a diagnostic process
that is conducted in phases of decreasing abstraction.

The present invention thus relates to diagnostic DSS that assists physicians
in
the process of MCM-oriented diagnosis. The invention emphasizes proper workup
of a presenting symptom, sign, abnormal test result or a combination of these
and
supports a hypothetico-deductive diagnostic process. The invention integrates

several notions in a novel way resulting in a multi-phase, anchor-based
information
9


CA 02702720 2010-05-04

model that uses abstract diagnosis groups. This multi-phase approach, which
revolves upon an anchor finding per each phase, enables efficiency in
conducting
the diagnostic process using a minimal effective set of clinical data items
(CDIs) in
each phase.

The DSS of the invention provides decision support for MCM-oriented
diagnosis meant to support expert and non-expert physicians in the process of
investigating clinical problems in all fields of medicine.

In a broader sense, the invention can be applied to any DSS, including DSSs
outside the medical field, wherein the different possible solutions to a
problem can
be investigated in predetermined phases. Each phase is characterized by an
anchor
that is investigated.

The present invention thus relates to a computerized medical diagnosis
method for assisting a health professional to diagnose a medical condition,
comprising the steps of:

(i) providing an anchor condition;

(ii) associating with the anchor condition a plurality of diagnoses whose
main clinical manifestation is the anchor condition;

(iii) associating with each diagnosis of the plurality of diagnoses a weight
that represents the likelihood that the diagnosis is manifested as the anchor
condition;

(iv) associating a plurality Clinical Data Items (CDIs) with each diagnosis
of the plurality of diagnoses given the anchor condition, each combination of
CDI
and a diagnosis is assigned a true, false or unknown value, wherein a true
value
represents an evoking strength in which the CDI evokes the diagnosis and a
false
value represents a penalty that the diagnosis receives if the CDI is not
present in a
patient;

(v) calculating a total weighted score for each diagnosis among the
plurality of diagnoses by adding for each diagnosis: the likelihood that the
diagnosis
is manifested as the anchor, the evoking strengths of each CDI that is true
for the

patient and for which a relationships of CDI evokes diagnosis exists and
subtracting


CA 02702720 2010-05-04

for each CDI that is absent in the patient the penalty weight for defined
penalty
relationships between the diagnosis and the CDIs;

(vi) choosing a diagnosis above a cutoff level of the weighted score and
making it an anchor condition; and

(vii) repeating steps (ii) through (vi) until the diagnosis with the highest
weighted score does not have any associated diagnoses and is thus determined
to be
the diagnosis for the medical condition.

In certain embodiments of the present invention, the anchor condition is a
symptom, a sign, a laboratory test result, an imaging test results or any
combination
thereof.

In certain embodiments of the present invention, the DSS of the invention
suggests clinical data items, laboratory, and imaging tests that should be
collected in
order to differentiate among alternative diagnoses.

In certain embodiments of the present invention, possible diagnoses of the
medical condition are listed and ranked.

In certain embodiments of the present invention, a knowledge base for
medical conditions and their associated main clinical manifestations is
created.

In certain embodiments of the present invention, the probability of
occurrence of a MCM given a disease is indicated.

In certain embodiments of the present invention, a user can override a
recommended diagnosis above the cutoff level of the weighted score and select
instead a different diagnosis and make it an anchor condition.

BRIEF DESCRIPTION OF DRAWINGS

Fig. 1 shows an instance of the Anchor class, as modeled using the Protege-
2000 modeling tool.

Figs. 2A-2B are screenshots showing the specification of an anchor, namly
Fever. Fig. 2A shows how Fever is a required CDI for the anchor of acute
diarrhea,
which is collected during the physical exam. Fig. 2B shows the allowed value
type
of this CDI: temperature. Temperature is an instance of Categorical-Value. The
categories of temperature are low or high.
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CA 02702720 2010-05-04

Fig. 3 shows an Entity-Relationship Diagram showing the information model
of the three relationships used to define an Anchor. Concepts are shown as
rectangles and relationships as diamonds. Multi-cardinality relationships are
marked
with n, in, p, and r.

Fig. 4 is a screenshot showing a definition of a temporal synergistic
relationship between a pair of CDIs that evokes a diagnosis in a given anchor.
The
evoking strength is specified by the slot "weight", whose range is 1-10. The
figure
shows that when the anchor is infections diarrhea, then the occurrence of
ingestion
of specific food 3 h (+3 h) before the onset of diarrhea suggests, with weight
10, the

diagnosis of toxin-borne diarrhea. But, when ingestion of suspicious food is
not
present in the patient, 9 points are penalized from the diagnosis of toxin-
borne
diarrhea.

Fig. 5 is a pseudo-code for the diagnoses scoring algorithm given an anchor.
Fig. 6 shows the scores of the hypotheses defined in the DD-set of the
current phase of the algorithm, shown in Fig. 1. Numbers appearing in italics
(the

first number in each row) are based on the Diagnosis_manifested_as_anchor
relationship. Numbers appearing in regular font are based on the
CDI_evokes_Diagnosis relationships (evoking strengths and penalties). The
scores
of the Dxs that are above the cutoff are shown in bold.

Fig. 7 shows Bayesian Networks (BN) for the three phases of diagnosis of
diarrhea derived from the knowledge represented in Table 2 with uniform prior
probabilities of disease hypotheses.
Fig. 8 shows a diagnostic knowledge for the anchor diarrhea.

Fig. 9 shows a diagnostic knowledge for the anchor acute diarrhea.
Fig. 10 shows a diagnostic knowledge for the anchor infectious diarrhea.
MODES FOR CARRYING OUT THE INVENTION

In the following detailed description of various embodiments, reference is
made to the accompanying drawings that form a part thereof, and in which are
shown by way of illustration specific embodiments in which the invention may
be
12


CA 02702720 2010-05-04

practiced. It is understood that other embodiments may be utilized and
structural
changes may be made without departing from the scope of the present invention.

A Main Clinical Manifestation (MCM)-oriented diagnosis is a problem-
oriented process that starts with a chief clinical problem, reasons about
possible
diagnoses that would be manifested as the MCM, and suggests the clinical data

items, laboratory, and imaging tests that should be collected in order to
differentiate
among alternative diagnoses. The MCM-oriented reasoning process is typically
conducted in phases. At the initial phases, the differential is sometimes
between
diagnosis groups that are meaningful in the context of the diagnostic process,
for

example, chronic vs. acute diarrhea or infectious vs. noninfectious diarrhea.
Such
diagnosis groups are referred to as abstract diagnoses. As the diagnostic
process
advances, the differential is between actual diagnoses. Such phased problem-
oriented processes are used for many clinical problems (e.g., syncope,
jaundice), as
seen in the classical clinical textbooks and in some specific medical books
and

evidence-based clinical practice guidelines [Tierney LM, Henderson M. The
patient
history: evidence-based approach. McGraw-Hill Medical; 2005; Denekamp Y,
Nasreldeen 0, Peleg M. Characterization of the knowledge contained in
diagnostic
problem oriented clinical practice guidelines. Proc AMIA Symp 2007:929].

The process of clinical investigation of clinical problems is complex and
requires using and analyzing a wide relevant set of clinical data items in a
systematic organized way. Physicians are expected to properly handle a wide
range
of clinical problem investigations. Yet incomplete workup was found to be a
major
source of quality of care problems [Balla U, Malnick S, Schattner A. Early
readmissions to the department of medicine as a screening tool for monitoring
quality of care problems. Medicine (Baltimore) 2008;87(5):294-300]. DSS can
aid
physicians to manage the investigations, avoiding unnecessary referrals,
unnecessary costly tests, or diagnostic errors, by empowering them with
updated
knowledge, evidence-based when possible. Representing and delivering such
knowledge can help overcome diagnostic errors that are due to cognitive
biases,

such as `confirmation bias', `outcome bias', or `overconfidence bias'
[Croskerry P.
13


CA 02702720 2010-05-04

The importance of cognitive errors in diagnosis and strategies to minimize
them.
Acad Med 2003;78(8):775-80].

The invention provides a computerized, MCM-based, medical diagnostic
DSS that relies on evidence-based clinical knowledge whenever available. It is
important to assess the availability of evidence-based (EB) sources, such as
clinical

practice guidelines, for aiding MCM-oriented clinical diagnosis utilizing
primarily
data types found during history and physical examination. To determine the
extent
at which clinical guidelines follow MCM-oriented diagnosis and report EB
statistics, a study was conducted [Denekamp Y, Nasreldeen 0, Peleg M.

Characterization of the knowledge contained in diagnostic problem oriented
clinical
practice guidelines. Proc AMIA Symp 2007:929] of diagnostic guidelines that
were
archived in the National Guideline Clearinghouse (NGC) web site (www.ngc.gov) -

a public resource for evidence-based clinical practice guidelines, initiated
and
maintained by the Agency for Healthcare Research and Quality and the US

Department of Health and Human Services. Filtering features were employed,
provided by NGC's website to consider only the potential diagnostic guidelines
(1182 guidelines, at the time of the study). Each guideline was then manually
inspected, and it was found that 146 of the guidelines indeed addressed
diagnosis
that starts with a MCM. After characterizing 25% of these guidelines, we found

little use of quantitative statistical data, such as frequency of
manifestation of
findings in given diseases and disease prevalence, for determining diagnosis.
That
trend found in the study, which was done in 2007, was also observed in an
updated
study that we recently summarized. In addition, it was found that many of the
guidelines make use of disease categories i.e., abstract diagnoses rather than
just
individual diagnoses. Some guidelines reported temporal and synergistic
relationships between patient findings, which serve as important knowledge for
diagnosis.

These findings suggest that although MCM-oriented diagnosis is a well
accepted diagnostic approach, MCM-oriented guidelines that report evidence-
based
14


CA 02702720 2010-05-04

statistical data are not very common, necessitating the elicitation of such
data from
other sources, such as experts or from statistical clinical databases.

The MCM-oriented approach of the invention uses the following six notions:
(1) MCM-oriented diagnosis
The medical diagnosis DSS of the invention enables MCM-oriented
diagnosis and emphasizes the use of clinical data items from the history and
physical examination; a MCM is any CDI or a combination of CDIs that can be a
symptom, sign, laboratory or other test, which is the starting point of the
diagnosis
process. Unlike other DSSs, in the invention the MCM is treated as being more

important than other findings and plays a crucial role at focusing the
diagnostic
process. The invention does not associate every disease with every finding, as
done
in diagnostic DSS for broad domains, nor does it represent its knowledge as a
network of interconnected frames of diseases and clinical states from which
disease
hypotheses are selected, based on findings exhibited by the patient, as done
in the

Present Illness program. The invention considers and ranks for each MCM only
the
set of diagnoses whose main clinical manifestation is the MCM, as reported in
evidence-based (EB) sources or medical literature that discuss problem-
oriented
diagnosis. This is expected to enable more efficiency and accuracy in scoring
the
different diagnosis hypotheses that are evoked by the MCM. For example, if the

MCM is hyponatremia (low level of sodium), then for the DSS of the invention,
pneumonia will not be part of the DD-set as it is in some other models. This
is
because although hyponateremia can be found in pneumonia, it will never be the
main clinical manifestation of pneumonia; a patient with pneumonia will
exhibit
other findings (e.g., fever, cough, rapid breathing, etc.) that will focus the
clinician
on pneumonia.

(2) Phases

The DSS of the invention presents a new approach by supporting a
diagnostic process that is carried out in predetermined phases. The DSS
supports
phases by structuring the process of DD reduction as a predetermined tree of

hierarchical DDs, which are referred to as DD-tree. Each layer of the tree


CA 02702720 2010-05-04

corresponds to a diagnostic phase (e.g., acute diarrhea, infectious diarrhea),
mimicking the clinician's hypothetico-deductive reasoning process of diagnosis
that
is used during problem-oriented diagnosis. At the beginning of the diagnostic
process, the focus (anchor) of the diagnosis is the MCM (e.g., diarrhea,
jaundice,

syncope) that triggered the diagnostic process, and serves as the root of the
tree. For
this anchor, a set of diagnoses that can be abstract and relevant clinical
data items
for making a diagnosis are defined. As shown in Table 2(a) for the diarrhea
anchor,
a set of two abstract diagnoses are provided in phase 1: acute vs. chronic
diarrhea.
To differentiate between them, the DSS uses the CDIs: "duration of diarrhea <
14

days" and "duration >14 days", which, per definition, are considered
pathognomonic (i.e., unambiguously characteristic of a particular disease) for
discriminating between these two alternatives. As the diagnostic process
advances
through the levels of the diagnostic tree, the DD-set becomes more and more
specific. For example, if in phase 1, the selected alternative was acute
diarrhea,

then, as shown in Table 2(b), acute diarrhea serves as the anchor for phase 2.
Phase
2 includes five alternatives, including infectious diarrhea, medication
change,
inflammatory bowel disease, intermittent bowel obstruction, and colonic
ischemia.
To differentiate among these hypotheses the DSS uses a collection of CDIs that
are
relevant for that phase, as shown in the second row of Table 2(b) and the user
enters

values to indicate whether these CDIs are present in the patient. The
strengths/probabilities of relationships between disease hypotheses and CDIs
in
each phase are indicated as numbers in the table, as explained below. Based on
the
CDIs values for the patient, an algorithm (heuristic or Bayesian) ranks the
diseases
in the DD-set and sets the highest ranking disease as the new anchor for the
next
phase.

Table 2 - A set of three tables showing a path in the DD-tree. Each table
represents
a different layer (phase) in the tree. The second column shows the disease
manifested as anchor relationship data. The other cells in the tables show
evoking

strengths and penalties (penalties are shown in parentheses) for diagnoses
given an
16


CA 02702720 2010-05-04

anchor. The anchor is diarrhea in phase 1, acute diarrhea in phase 2, and
infectious
diarrhea in phase 3. The numbers in table (b) were elicited from experts and
the
numbers from tables (a) and (c) were elicited from EB sources.

(a) Phase 1 anchor: diarrhea CDI
Diagnosis Dx manifested as Duration <14 days Duration >14 days
diarrhea
Acute pathognomonic
Diarrhea
Chronic pathognomonic
Diarrhea

(b) Phase 2 anchor: acute diarrhea CDI
Diagnosis Dx manifested High Abrupt Nausea/ Mucus Arthritis More people
as acute diarrhea fever presentation vomiting developed
Infectious 16 9 9 8 1 9
Diarrhea
Medication 8 7 3
change
Bowel Inflammatory 14 4 2 2 8 (-5)
Disease
Intermittent 10 2 2
Bowel
Obstruction
Colonic 8 2 2
Ischemia
(c) Phase 3 anchor: infectious CDI
diarrhea
Diagnosis Manifested Abdomina Tenesmus Nausea Watery Flu-like Bloody Recent
as infectious Fever I pain /vomit diarrhea sympto stools antibioti
diarrhea in
Shigella 8(-7) 8 7(-7) 8 4
Non-Shigella 7(-5) 7 4 3
bacterial
Clostridium 3 2 8(-6)
Parasitic
1 3
Food-borne* 0

Viral 8 2 8 9 7(-8)
* Disease-finding relationships for the food-borne diagnosis are provided in
Table 3.
(3) Abstractions

As explained hereinabove, the multi-phase diagnostic process of the DSS
often starts with abstract concepts, is refined in each phase, and ends in
specific
diagnoses. This diagnostic process that uses abstractions is valuable not only

because of the efficiency of CDIs considered in each phase, but also because
17


CA 02702720 2010-05-04

abstractions are often used by clinical experts during problem-solving. As
discussed
by Newell and Simon [Newell A, Simon HA. Human problem solving. Englewood
Cliffs, NJ: Prentice Hall; 1972], studies examining constrained problem spaces
such
as chess-playing have documented that experts recognize patterns of activity
within

a domain at an integrated, higher level ("chunking") than novices.
Abstractions have
been used in diagnostic DSSs before. Abstractions have been used in the
Internist-1
[Pople Jr HE. Heuristic methods for imposing structure on ill-structured
problems:
the structuring of medical diagnostics. In: Szolovits P, editor. Artificial
intelligence
in medicine. Boulder, Colorado: Westview Press; 1982.] knowledge base, which

contains a hierarchy of disease categories, organized primarily around the
concept
of organ systems, where positive findings can evoke either individual disease
nodes
or higher-level nodes in the disease hierarchy. Pople suggested a reasoning
model
where any given disease can be classified in as many descriptive categories of
the
hierarchy as are appropriate.

(4) Anchor-specific disease-finding relationships

The DSS of the invention provides weighted relationships between disease
and findings that are specific to the given anchor and to a given geographical
location, including (a) the DD-set that is relevant and probable for the given
MCM
(first column in Table 2); and (b) the set of relevant findings that can
distinguish

among the diagnoses in the DD set (the top row in each table of the Table 2
table
set). (c) the likelihood of a diagnosis to be manifested as the anchor (second
column
in Table 2) - a notion that is unique to the invention. This is used to rank
higher
diagnoses that are usually manifested as the anchor finding. For instance, for
an
anchor of syncope, the DD-set includes cardiac arrhythmias and pulmonary

embolism, yet cardiac arrhythmias are more likely to be manifested as syncope
than
pulmonary embolism; (d) the evoking strength with which a finding suggests a
diagnosis in the DD set (numbers in the cells of Table 2). Unlike the use of
this
feature in Internist-1 (where it was first introduced), QMR, DXplain, and the
Present Illness program, the evoking strength in the DSS of the invention
considers

just the disease hypotheses that are relevant for the anchor finding (which,
at the
18


CA 02702720 2010-05-04

beginning of the process is the MCM); (e) the penalty that a disease
hypothesis
should receive in the absence of a finding (numbers given in parentheses in
the cells
of Table 2). Penalties are also used in other diagnostic DSSs, such as
Internist-1
(where they were first introduced), QMR, DXplain, and the Present Illness
program.

However, in the DSS of the invention, the size of this penalty is proportional
to the
frequency at which the disease exhibits the finding; and (f) synergistic
effect
between findings that together suggest a diagnosis with greater certainty than
the
sum of the two is explicitly modeled (Table 3); these relationships may be
temporal
(e.g., fever before rash, Jaundice after fever) or not temporal, just two
findings that
together strengthen a diagnosis.

Table 3 - A table for eliciting synergistic temporal relationships between
CDIs and
diagnoses, for the anchor of infectious diarrhea

Diagnosis CDI-1 CDI-2 (penalty) Time between Weight
two CDIs

Food-borne Time of onset Time of ingestion 0-6 hours 10 (-9)
diarrhea of diarrhea of suspicious food

(5) Computational model

The DSS of the invention's information model can be combined with
different computational models (a heuristic model and a Bayesian model) for
scoring disease hypotheses. This feature was guided by the Bayesian
formulation of
the heuristic algorithm of QMR. Uniquely, in the DSS of the invention the
Bayesian

formulation is structured according to the phases of anchors used in the MCM-
oriented diagnostic process. The last three relationships between disease and
findings discussed above are used with the heuristic scoring algorithm,
discussed in
below. The Bayesian approach, also discussed below, uses prior probabilities
for
each disease hypothesis and conditional probabilities for each combination of
finding and disease (finding frequencies).

19


CA 02702720 2010-05-04
(6) User control
The invention's approach allows the user to follow the diagnostic process
with any diagnosis in the DD-set, even if it is not the highest-ranking one.
This
feature is in accordance with the modern view of DSSs as providing assistance
to a

user who is in charge of the clinical process rather than being an
authoritarian and
inflexible approach towards solving the clinical task for the user. This
property is
important because no computer program can know all that needs to be known
about
the patient case, no matter how much time or effort is spent on data input
into the
computer system, and therefore the clinician user who directly evaluated the
patient

must be considered to be the definitive source of information about the
patient
during the entire course of any computer based consultation.

II. The DDS information model of the invention

The main class in the DSS of the invention's model is Anchor, which
represents the MCM in the first phase of the diagnostic process. Anchor is
defined
using three structural slots and three relationship slots. The relationship
slots store
knowledge that is used by the DSS algorithm to score diagnoses in the DD-set.
Fig.
1 shows an instance of the Anchor class, as modeled using the Protege-2000
[Gennari J, Musen MA, Fergerson RW, Grosso WE, Crubezy M, Eriksson H, et al.

The evolution of protege: an environment for knowledge-based systems
development. Int J Hum Comput Interact 2002;58(1):89-123] modeling tool.
Structural Slots

anchor concepts - the concept (or concept combination, such as fever and
rash) on which the DD is focused at the current diagnosis phase. In Fig. 1,
the
anchor concept is Acute Diarrhea.
relevant-diagnoses-or-abstractions - the relevant diagnoses for the
current DD phase. At initial DD phases, abstractions are often used instead of
final
diagnoses.

relevant CDIs - the CDIs that should be collected in order to select the
most probable diagnosis from the DD set. For each CDI the medical concept is


CA 02702720 2010-05-04

specified and whether it is a required value for a certain phase of the DD
process
given a certain anchor, as shown in Fig. 2.

Relationship slots

The following types of relationships relate CDIs to diagnoses. They are used
by the algorithm that scores the diagnoses in the DD-set. Fig. 3 shows the
information model of these three relationships, using an Entity-Relationship
(ER)
Diagram [Hoffer JA, George JF, Valacich JS. Modern system analysis and design.
3rd ed. Prentice Hall; 2002] - where each information class is represented by
an
entity type (depicted as a rectangle) and relationships between information
classes

are represented by a relationship type (depicted as a diamond). The ER
notation is
used because it is widely familiar and simple and often is used to represent
information models. Multi-cardinality relationships are marked with n, m, p,
and r
in Fig. 3. For example, (a) represents the following statement: "Anchor is
manifested as n (many) Diagnoses or Abstractions". Properties of relationships
are

written below the diamond symbols. Examples of entity relationships instances
are
provided in parentheses. The arrows mark the directionality in which the
relationship should be read. (a) Diagnosis_Manifested_ As-Anchor - the example
shows that a diagnosis of Hepatitis B is manifested as jaundice with a
probability of
17 (out of 20); (b) CDI_Evokes_Diagnosis_In_Anchor - the CDI fever with a

value of high or low, in an anchor of infectious diarrhea evokes the diagnosis
Bacterial (non-Shigella) diarrhea with an evoking strength of 7 (out of 10)
and a
frequency_penalty of 5; (c) Temporal CDI Relationship
Given_Diagnosis_For_Anchor - If the time of ingesting suspicious food is 3 3
h
before the time of onset of diarrhea, in an anchor of Infectious Diarrhea, the
diagnosis of toxin-borne diarrhea is evoked with weight of 10 (out of 10) and
9
points are penalized from that diagnosis hypothesis for the absence of
ingestion of
suspicious food (frequency_penalty).

diagnoses_manifested_as_anchor - bonus points are given to diagnoses
that are usually manifested as the anchor concept(s). Often, the anchor
concept
holds a MCM that is a patient's CDI. However, the anchor may alternatively be
an
21


CA 02702720 2010-05-04

abstraction used in the DD (part of the DD-set), such as in the case of
infectious
diarrhea, which is an abstract anchor concept and not the MCM (the MCM is
diarrhea). For example, cardiac arrhythmias are often manifested as syncope
(anchor), but pulmonary embolism, which is in the DD-set of the syncope
anchor, is
usually not manifested as syncope.

CDI_evokes_Diagnosis - Like the Internist- 1/QMR system, the DDS of the
invention considers CDIs that suggest a diagnosis, with a certain evoking
strength
(1-10). As in Internist-1, frequency_penalty stores the frequency at which a
finding
is found in a disease; when the patient does not exhibit a CDI that is
frequent in a
disease hypothesis, points can be deducted from that hypothesis.

Synergistic_CDI_Relationship_Given_Diagnosis_ForAnchor - in some
cases, when combinations of two (or more) CDIs occur together or in a certain
temporal pattern, this suggests a certain diagnosis more probable than the
additive
contribution of each one of the CDIs alone. For example, if it is known that
diarrhea

developed less than six hours after ingesting suspicious food, it suggests the
diagnosis of toxin-borne Diarrhea, based on the combination of the diarrhea
and
ingestion of suspicious food as CDIs, as shown in Fig. 4. Fig. 4 shows a
definition
of a temporal synergistic relationship between a pair of CDIs that evokes a
diagnosis in a given anchor. The evoking strength is specified by the slot
"weight",

whose range is 1-10. The figure shows that when the anchor is infections
diarrhea,
then the occurrence of ingestion of specific food 3 h ( 3 h) before the onset
of
diarrhea suggests, with weight 10, the diagnosis of toxin-borne diarrhea. But,
when
ingestion of suspicious food is not present in the patient, 9 points are
penalized from
the diagnosis of toxin-borne diarrhea.

A Bayesian model for scoring disease hypotheses
The DDS model of the invention can be formulated in Bayesian terms with
certain simplifying assumptions. As was done for the Bayesian formulation of
the
QMR knowledge base, it can be assumed that findings are conditionally
independent given any disease hypothesis (therefore the probability of having

multiple findings given a hypothesis is the product of probability of having
one
22


CA 02702720 2010-05-04

finding given the hypothesis, for all findings in the set of findings).
Therefore,
temporal synergistic relationships were converted between two findings and a
disease hypothesis into one finding. As in QMR-DT, the influence of multiple
diseases can be modeled on a finding assuming causal independence (i.e., the

probability of a finding given only one disease is present P(FID) instead of
given
combinations of diseases. In this way, all the conditional probabilities that
are used
in the Bayesian Networks (BNs) are of the form P(FD), representing frequency
data. It can also be assumed that only one of the alternative hypotheses would
be
present in a patient.
To take advantage of the phased model of the DDS of the invention, the
knowledge is arranged in sets of small BNs, where each network corresponds to
one
phase of DDS of the invention's knowledge-base (KB) and includes the relevant
findings and disease hypotheses for that phase, the prior probabilities of the
disease
hypotheses and the conditional probabilities P(FD). Parts a, c, and d of Fig.
7 show

BNs that correspond to the example discussed hereinabove. Fig. 7 shows
Bayesian
Networks (BN) for the three phases of diagnosis of diarrhea derived from the
knowledge represented in Table 2 with uniform prior probabilities of disease
hypotheses. (a) Phase 1 BN. The top insert shows the prior probability of the
two
abstractions: acute and chronic diarrhea. The uniform prior probabilities are
shown

for illustrative purposes. The bottom insert shows the conditional
probabilities of
P(FD); (b) BN for computing prior probabilities of disease hypotheses of phase
2
BN, relying on the "diagnosis manifested as anchor" relationships of Table
2(b); (c)
BN corresponding to phase 2. The prior probabilities are those derived by
computing the posterior probability based on the BN shown in (b). The
conditional
probabilities for P(high feverID) are shown for illustrative purposes; (d) BN
corresponding to phase 3. The BN were created using the GeNle tool
(http://genie.sis.pitt.edu/) and reproduced in the figure.

Because the DDS of the invention's model is arranged in diagnostic phases,
in the move from a BN of one phase to the BN of the next phase, simply refines
the
diagnosis without the need to combine the numerical results from previous
phases.
23


CA 02702720 2010-05-04

For example, in phase 1, setting the value of duration to <=14 and updating
the
model will result in a posterior probability of 1 for acute diarrhea. Given
that
abstract diagnosis, we proceed to the phase 2 BN whose anchor is acute
diarrhea.

To distinguish the main clinical manifestation from other manifestations of a
disease, the information contained in the disease-manifested-as-anchor
relationships of the DDS of the invention is used to update the values of the
posterior probabilities of the disease hypotheses. To do so, another BN is
constructed consisting of the diseases (or abstractions) used in the DD-set of
a
phase and the phase's anchor (see Fig. 7-B). The probabilities used in this
network

would be the prior probabilities of the diseases. The conditional
probabilities
relating the anchor to the individual disease hypotheses P(anchorID) are
derived
from the disease-manifested-as-anchor relationships. From this small network,
the
posterior probability for a disease hypothesis can be derived given that the
anchor is
present. These probabilities can now serve as the prior probabilities for the
diseases

in the BN composed of the diseases in the DD-set for the phase and all the
other
findings apart from the MCM as shown in Fig. 7-C.

Using the above Bayesian approach requires having prior probabilities for
each disease hypothesis and conditional probabilities for each combination of
finding and disease (finding frequencies). In QMR-DT, the prior probabilities
were

assembled from data compiled by the National Center for Health Statistics on
inpatients discharged from short-stay nonfederal hospitals and the conditional
probabilities P(FID) were derived from the QMR frequency data. Preferably,
data
used by the DDS of the invention is based on EB studies. However, as
hereinbelow,
many of the required statistical values are not found in the EB sources.

The DDS diagnosis-scoring algorithm
The heuristic scoring algorithm of the DDS of the invention requires the
input of fewer probabilities than the Bayesian methods discussed hereinabove.
The
algorithm, whose pseudo-code is shown in Fig. 5, considers CDIs that suggest a
diagnosis with a certain evoking strength. If the CDI is present, then points
are

awarded according to the evoking strength. If the CDI is considered to be
24


CA 02702720 2010-05-04

pathogneumonic in evoking the diagnosis, as is sometimes the case for
laboratory or
imaging findings and very rarely for history and physical examination
findings, then
that diagnosis would be concluded. If, however, the CDI is not present penalty
can
be used to deduct points for certain disease hypotheses. The size of the

frequency_penalty indicates the likelihood of a hypothesis being inappropriate
when
a certain CDI is absent.
The last component used for scoring diagnoses is
Synergistic_CDI_Relationship_Given_ Diagnosis_For_Anchor. When such a
relationship is defined and both CDIs are present, points are added to the
relevant

diagnosis, according to the weight defined for that relationship. If not both
CDIs are
present, we use selective penalty to deduct points from the diagnosis.

After all the diagnoses in the DD-set are scored, the cutoff - diagnoses are
calculated, and the diagnoses that are below the cutoff value do not continue
to
future steps of the algorithm. In certain embodiments of the invention, the
cutoff

value is calculated as a value that is 10% lower than the score of the highest
ranking
diagnosis. The algorithm will suggest the diagnoses that are above the cutoff.
If a
disease hypothesis was selected via a pathogneumonic finding, then no cutoff
value
is necessary - only that hypothesis is suggested. However, the user may choose
to
override the recommended diagnosis and select a different diagnosis from the
DD-

set for the current anchor. The system can then set this diagnosis as the
anchor for
the next diagnosis phase, taking the information for the appropriate phase
from the
respective node in the DD-tree. The systems does not allow the user to jump to
any
node in the DD-tree; if the user has already reached a certain anchor it is
interpreted
to mean that he has accepted all the abstractions leading to that node. If
this is not
the case, the user can start another session.

Fig. 6 provides an example of one phase of the algorithm, based on the
knowledge shown in Fig. 1. That knowledge was adapted from a medical book
based on the principles of evidence-based MCM-oriented diagnosis [Tierney LM,
Henderson M. The patient history: evidence-based approach. McGraw-Hill

Medical; 2005] and from the infectious diarrhea guideline [Guerrant RL, Gilder
TV,


CA 02702720 2010-05-04

Steiner TS, Thielman NM, Slutsker L, Tauxe RV, et al. Practice guidelines for
the
management of infectious diarrhea. Clin Infect Dis 2001;32(3):331-51], as
explained hereinbelow. The full run of the algorithm can be found in Appendix
A.
The patient for which the algorithm was executed has been having high fever
for 7

days, nausea, vomiting, bloody stools, abdominal pain, and tenesmus. The
symptoms appeared abruptly it was not known whether other people had the
disease. However, it was known that the following findings are not present:
ingestion of suspicious food, mucus, antibiotics, flu-like symptoms, and
arthritis.

Examining the score of the first diagnosis, Infectious Diarrhea, it can seen
that 16 points (out of 20) were awarded based on the fact that this diagnosis
is
usually manifested as diarrhea (the anchor), and 9, 8, and 8 points (out of 10
maximum points per finding) were awarded based on the CDI_evokes_diagnosis
links between this diagnosis and the following CDIs: fever (high), abrupt
presentation, and nausea/vomiting. Note that the relationship of arthritis
evokes

inflammatory bowel disease with a frequency penalty of 5 was used to deduct
points from that disease hypothesis because arthritis was not present.

Developing a DDS knowledge base
In the DDS approach of the invention, which is centered on a MCM, the set
of disease hypotheses and CDIs considered at each diagnosis phase as well as
the
relationships between findings and disease hypotheses depend on the MCM.

Therefore, knowledge added to the DDS knowledge base to support diagnosis of a
new MCM is independent of the knowledge that already exists in the knowledge
base for existing MCMs. This has several consequences. First, phase-specific
knowledge usually cannot be reused for different MCMs. For example, while

arrhythmias are considered as disease hypothesis for a syncope MCM and for a
palpitations MCM, different arrhythmias are considered for each MCM, and with
different likelihoods. However, as we advance toward more specific phases in a
DD-tree (e.g., bradyarrhythmia), it is more likely that these phases could be
reused
for the DD-trees of different MCMs. A second consequence of the independence
of

MCM-oriented diagnostic knowledge is that the addition of knowledge for a new
26


CA 02702720 2010-05-04

MCM will not affect system performance, because only the DD-tree for that MCM
would need to be considered. A third consequence is that the process of
developing
the DDS knowledge for different MCMs can be done independently and in
parallel.

The steps involved in developing the DSS knowledge needed to support
diagnosis of a MCM is discussed hereinbelow, addressing the level of effort
needed.
This is based on our experience in developing the knowledge for the diarrhea
MCM
and for ongoing development of the syncope MCM. Whenever possible, it was best
to elicit disease hypotheses, relevant CDIs, evoking strengths, frequencies of
manifestations, weights of diagnosis manifested as anchor, and weights of

synergistic relationships based on EB studies. The disease hypotheses that
were
considered for the diarrhea case and the CDIs used to distinguish between them
were based on EB sources [Tierney LM, Henderson M. The patient history:
evidence-based approach. McGraw-Hill Medical; 2005; Guerrant RL, Gilder TV,
Steiner TS, Thielman NM, Slutsker L, Tauxe RV, et al. Practice guidelines for
the

management of infectious diarrhea. Clin Infect Dis 2001;32(3):331-51]; Table 2
shows the disease hypotheses (first column) that were considered and the CDIs
(top
row) used to differentiate among them.

Some EB studies report probabilities of manifestation of a finding given a
disease (frequency). In the DSS of the invention, the important probabilities
for the
heuristic algorithm are the evoking strengths, i.e., the probabilities of
disease given

a finding P(DIF) for a given anchor. However, these probabilities are usually
not
available in evidence-based studies. Bayes law can be used to convert the
frequency
data into P(DIF) based on the prior probabilities of diseases and of findings
per a
given anchor. However, the prior probabilities of a finding (per anchor) are
difficult

to find, and the prevalence of common etiologies of diarrhea in a primary care
setting is reported to be unknown in the EB source that we used. Nevertheless,
since
for a given anchor, a small set of relevant diagnoses (or abstract diagnosis
groups)
are considered as the DD-set, the frequency numbers P(FID) were used to select
the
most relevant diagnosis in the limited DD-set, assuming uniform prior
probabilities

of diseases (prevalence). In this way, the disease in which the CDIs exhibited
by the
27


CA 02702720 2010-05-04

patient are most frequent is the disease that should be evoked. The frequency
numbers were converted to a scale of 0.. 10. When ranges were reported, the
average
was used. Frequency data were provided in the clinical guideline [Guerrant RL,
Gilder TV, Steiner TS, Thielman NM, Slutsker L, Tauxe RV, et al. Practice

guidelines for the management of infectious diarrhea. Clin Infect Dis
2001;32(3):331-51.] that was used for the different diseases belonging to the
infectious diarrhea abstraction (Fig. 2C). Evoking strength data for the non-
infectious acute diarrhea (Fig. 2B) were supplied by experts.

The frequency_penalty used to subtract points in a selective way from a
hypothesis when the patient does not exhibit a finding that is manifested in
high
frequency in a disease, was provided by two clinical experts, who consulted
the
frequency values reported in the EB studies, but used their expert opinion to
decide
about the selective penalty (see the Diagnosis-Scoring Algorithm above). These
experts also provided numbers for other relationships for which no data were

reported in the EB studies: the synergistic effects (scale of 1..10) and
Diagnosis_Manifested_As_Anchor (scale of 1..20) in the context of diarrhea.

To elicit from experts the frequencies, penalties, and weights for diagnosis
manifested as anchor for our preliminary study, Excel tables were prepared
such as
those shown in tables 2 and 3. The structure of the tables was set based on
evidence-

based sources and the numbers were supplied by two experts by consensus
formulation.

When eliciting such data from experts for a more comprehensive evaluation
study, it is suggested to follow the methodology for knowledge base
construction
based on expert opinion that was proposed by van Ast et al [van Ast J, Talmon
JL,
Renier WO, Hasman A. An approach to knowledge base construction based on
expert opinions. Methods Inf Med 2004;43(4):427-32]. That methodology suggests
starting with a group of experts and calculating the inter-rater interclass
correlation
coefficient; if it is not large enough, the Spearman-Brown prophecy can be
used to
predict the number of additional experts.

28


CA 02702720 2010-05-04

The effort required to develop the DSS knowledge for a given MCM is
considerable. Based on our experience, gathering information from evidence-
based
sources and arranging it in phases of disease hypotheses and CDIs used to
distinguish among them required less effort than acquiring the numbers (which

include frequencies, penalties, and weights for diagnosis manifested as
anchor) that
were not available in the EB studies. Working with the experts requires
several
iterations; in the first iteration, which spans several sessions, the experts
supply all
the requested numbers using the tables that were prepared. Then a statistical
examination of expert agreement is conducted to see if the numbers could be

averaged. As noted above, establishing agreement between experts may require
using additional experts. After agreement is established the numbers are
entered
into the knowledge base and the system's performance on test cases is
evaluated, as
described in the next section. Fine tuning the knowledge base to support the
initial
set of test cases requires further iterations with the experts.


Preliminary evaluation studies
The DSS model of the invention was tested and refined by examining the
MCM-oriented diagnostic process of diarrhea. As EB sources of medical
knowledge, a medical book of problem-oriented diagnosis was used [Tierney LM,

Henderson M. The Patient History: Evidence-Based Approach: McGraw-Hill
Medical; 2005] and a guideline [Guerrant RL, Gilder TV, Steiner TS, Thielman
NM, Slutsker L, Tauxe RV, et al. Practice guidelines for the management of
infectious diarrhea. Clin Infect Dis 2001;32(3):331-51] for diagnosing
infectious
diarrhea. Screenshots from the DSS model of that guideline are shown in Figs.
1, 2
and 4. The encoding was validated using the diarrhea test case. An example of
the
DSS heuristic algorithm run on the diarrhea test case is provided in Appendix
A. 8
case vignettes were used to develop and fine-tune the diarrhea knowledge base
and
10 additional test cases to validate it, using the heuristic algorithm. All
the test cases
are presented in Appendix B. The test cases and two of the training set cases
were

developed by a clinician who was not involved in the development of the DSS
and
29


CA 02702720 2010-05-04

had no knowledge of it. The DSS of the invention produced the expected results
for
all the test cases. In one test case, the DSS could not differentiate between
two
diagnoses. The higher-ranking diagnosis (Bacterial diarrhea, non-Shigella) was
the
correct one, but it received a score that was just one point higher than the
diagnosis

of Shigellosis. However, even experts find it hard to differentiate these two
diagnoses from the presenting clinical data items.

In a study conducted, the knowledge base creation methodology described
was used for the clinical problem of Syncope. The algorithm of the invention
correctly classified cases taken from the medical literature.

The same test case shown in Appendix A was executed on several diagnostic
DSS for broad domains: QMR, DXplain, and GIDEON. The purpose was to see
how probabilistic diagnostic DSSs for broad domains perform in supporting the
process of an investigation of a clinical problem (e.g., diarrhea). If they
would
perform well in MCM-oriented diagnosis -a task for which they were not

designed- there would not be a need for special-purpose diagnostic DSSs. The
results are shown in appendices C, D, and E, respectively. As can be seen,
entering
a single clinical manifestation (acute diarrhea into QMR and bloody diarrhea
into
DXplain) produced a DD-set that does not use abstractions but contains
concrete
diagnoses. In the DD-set, the correct diagnosis (Shigellosis) was not one of
the top

diagnoses (above 35%) in QMR. In DXplain, it was the fourth diagnosis in the
rare
disease list. QMR does not guide the user as to what additional data should be
collected to distinguish among diagnoses, so we entered the case findings
unaided,
to refine the DD-set. Once again, Shigellosis was not the top scoring
diagnosis.
Moreover, the first score in QMR, Toxin-borne diarrhea, ranked extremely low
in
the DSS of the invention (because it is known that the patient did not ingest
suspicious food) and was eliminated by it. Similar results were obtained with
DXplain. Although DXplain asked the user about additional findings that may be
present, most of them were not relevant to differentiate Shigellosis from the
other
diagnoses in the DD-set. This strengthens the advantage of the DSS of the
invention
in supporting efficient investigations of clinical problems.



CA 02702720 2010-05-04

Running the diarrhea case in GIDEON produced better results than the QMR
and DXplain runs. Upon entering the single problem "diarrhea", the correct
diagnosis (Shigellosis) was ranked first. Note that, GIDEON normally prompts
the
user to input a few other parameters (not just one finding): disease onset
time and

geographical location. Entering the case's values for these parameters changed
the
DD; Shigellosis was no longer the top diagnosis. GIDEON did not guide us as to
what other finding we should be looking for. After entering the other findings
in the
test case, GIDEON correctly identified Shigellosis as the top diagnosis, well
separating it from the other diagnoses in the DD-set. However, GIDEON contains

knowledge just for infectious diseases. Thus, naturally, GIDEON will not help
in
diagnosing inflammatory (non infections) or medication-change related
diarrhea.
Many alterations and modifications may be made by those having ordinary

skill in the art without departing from the spirit and scope of the invention.
Therefore, it must be understood that the illustrated embodiment has been set
forth
only for the purposes of example and that it should not be taken as limiting
the
invention as defined by the following invention and its various embodiments.

Therefore, it must be understood that the illustrated embodiment has been set
forth only for the purposes of example and that it should not be taken as
limiting the
invention as defined by the following claims. For example, notwithstanding the
fact

that the elements of a claim are set forth below in a certain combination, it
must be
expressly understood that the invention includes other combinations of fewer,
more
or different elements, which are disclosed in above even when not initially
claimed
in such combinations. A teaching that two elements are combined in a claimed
combination is further to be understood as also allowing for a claimed
combination
in which the two elements are not combined with each other, but may be used
alone
or combined in other combinations. The excision of any disclosed element of
the
invention is explicitly contemplated as within the scope of the invention.

The words used in this specification to describe the invention and its various
embodiments are to be understood not only in the sense of their commonly
defined
meanings, but to include by special definition in this specification
structure,
31


CA 02702720 2010-05-04

material or acts beyond the scope of the commonly defined meanings. Thus if an
element can be understood in the context of this specification as including
more
than one meaning, then its use in a claim must be understood as being generic
to all
possible meanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,
therefore, defined in this specification to include not only the combination
of
elements which are literally set forth, but all equivalent structure, material
or acts
for performing substantially the same function in substantially the same way
to
obtain substantially the same result. In this sense it is therefore
contemplated that an

equivalent substitution of two or more elements may be made for any one of the
elements in the claims below or that a single element may be substituted for
two or
more elements in a claim. Although elements may be described above as acting
in
certain combinations and even initially claimed as such, it is to be expressly
understood that one or more elements from a claimed combination can in some

cases be excised from the combination and that the claimed combination may be
directed to a sub-combination or variation of a sub-combination.
Insubstantial changes from the claimed subject matter as viewed by a person
with ordinary skill in the art, now known or later devised, are expressly
contemplated as being equivalently within the scope of the claims. Therefore,

obvious substitutions now or later known to one with ordinary skill in the art
are
defined to be within the scope of the defined elements.

The claims are thus to be understood to include what is specifically
illustrated and described above, what is conceptually equivalent, what can be
obviously substituted and also what essentially incorporates the essential
idea of the
invention.

Appendix A. An algorithm run for diarrhea using TiMeDDx of the invention
The patient for which the algorithm was run has been having fever for 7
days, nausea, vomiting, bloody stools, abdominal pain, and tenesmus. The

symptoms appeared abruptly and it was not known whether other people had the
32


CA 02702720 2010-05-04

disease. However, it was known that the following findings are not present:
ingestion of suspicious food, mucus, antibiotics, flu-like symptoms, and
arthritis.
The knowledge in the TiMeDDx knowledge-base of the invention in shown

in Figs. 8-10, showing diagnostic knowledge for the anchors: diarrhea,
infectious
diarrhea and acute diarrhea.
The algorithm run is executed in three stages. Below, we show the scores
given by the algorithm to the different hypotheses. The algorithm is provided
and
explained in Section 4.

Stage 1:

H1: Acute Diarrhea +10+100 = 110
H2: Chronic Diarrhea 0

Cutoff is 99
Acute Diarrhea is selected as the new anchor.
Stage 2:

H1.1 Infectious Diarrhea +16+9+8+8=41
H1.2 Medication Change +8+7+3=18

H1.3 Inflammatory Bowl Disease +14+4+2+2-5=17
H 1.4 Intermittent Bowel Obstruction +10+2+2=14
H1.5 Colonic Ischemia +8+2+2=12
Cuttoff is 29

Infectious Diarrhea is selected as the new anchor

Stage 3

H1.1.1 Non-shigella Bacterial Diarrhea +7+7+3+4=21
H1.1.2 Parasitic Diarrhea +1+3 = 4

H1.1.3 Bacterial, antibiotics-associated Diarrhea (Clostridium) +3+2-6 = -1
H1.1.4 Shigella 8+8+4+8=28

33


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HI. 1.5 Toxin-borne diarrhea -9

H 1.1.6 Viral: 8+2+8-8
Cutoff is 25
Shigella is selected as the final diagnosis
Appendix B. Test cases for the diarrhea case
Training set:
Case 1: 7 days diarrhea, high fever, nausea and vomiting, abdominal pain,
bloody stools,
abrupt presentation, tenesmus, no flu-like, no suspicious food, no
antibiotics, no arthritis
Dx: Sigella

Case 2: 26 years old woman, diarrhea and low grade fever for 8 days, mucus
secretions,
signs of arthritis in the physical examination, no bloody stools , no
suspicious food, no
antibiotics, no tenesmus
Dx: Inflamatory

Case 3: 65 years old man, hospitalized due to pneumonia, is treated by
intravenous
antibiotic (cefuroxime), second day of diarrhea, no fever, abdominal pain, no
bloody stool,
no suspicious food, no antibiotics, no tenesmus
Dx: Clostridium

Case 4: 30 years old man, suffers from diarrhea 12 days, no fever, no
antibiotic, no
ingestion of suspicious food, no pain, no tenesmus, no bloody stools, no flu-
like symptoms
Dx: Parasitic

Case 5: 48 years old woman, 3 days diarrhea, abdominal pain, nausea and
vomiting,
bloody stool, no tenesmus, no antibiotic, no ingestion of suspicious food, no
flu-like
symptoms
Dx: Bacterial

Case 6: 27 years old, 5 days diarrhea, abdominal pain, nausea and vomiting,
tenesmus,
no bloody stools, no fever, no ingestion, no antibiotics, no ingestion of
suspicious food, no
flu-like
Dx: Shigella

Case 7: 37 years old male, basically healthy, 2 days bloody diarrhea,
abdominal pain, fever
38.2 c, no history of antibiotic treatment, no ingestion of suspicious food,
no flu-like, no
tenesmus
Dx: Shigella

Case 8: 25 years old male, 5 hours of watery diarrhea, abrupt abdominal pain,
nausea, no
fever, ate at a hamburger place, no antibiotics, no tenesmus, no flu-like
Dx: Food-borne

34


CA 02702720 2010-05-04
Test cases:
Case 9: 70 years old female, 3 days watery diarrhea, abdominal pain, no fever,
no nausea,
treated in the last week with antibiotic for acute Cellulitis, no ingestion of
suspicious food,
no tenesmus, no flu-like
Dx -Clostridium

Case 10: 22 years old female, 2 days of watery diarrhea, fever 38 c, cough,
headache,
abdominal pain, no antibiotic, no ingestion of suspicious food, no tenesmus
Dx - Viral gastroenteritis

Case 11: 32 years old male, volunteered in Sudan, diarrhea for 10 days, no
fever,
abdominal pain, no bloody stools, no antibiotic, no suspicious food, no
tenesmus, no flu-
like
Dx - Parasitic

Case 12: 40 years old female, kinder garden teacher, 3 days of diarrhea,
bloody, low grade
fever, no nausea, abdominal pain, no antibiotic treatment, no suspicious food,
no tenesmus,
no flu-like
Dx - Bacterial non-shigella

Case 13: 32 years old male, 3 days of bloody diarrhea 15 times per day, 39 c
fever, very
strong abdominal pain, nausea and vomiting, tenesmus, no flu-like, no
antibiotic, no
suspicious food
Dx - Shigella

Case 14: 24 years old female, 6 days diarrhea , no blood, but with rubbery
secretions,
abdominal pain, nausea but no vomiting, 38.5 c fever, no antibiotic treatment,
no
suspicious food, no flu-like, no tenesmus
Dx - Bacterial and possibly Shigella

Case 15: 28 years old female, no of history of any illness, 5 days diarrhea,
no abdominal
pain, no fever, no nausea and vomiting, 10 days ago started antibiotic for
urinary tract
infection, no tenesmus, no suspicious, no flu-like
Dx - Clostridium

Case 16: 25 years old male, 10 days diarrhea, not bloody, no fever, diffuse
abdominal pain,
returned from a trip in South America, no tenesmus, no flu-like, no
suspicious, no
antibiotics
Dx - Parasitic

Case 17: A 42 years old man, 1 day of diarrhea, not bloody, nausea and
vomiting, diffuse
periodic abdominal pain, no fever, a few hours ago ate at a new fish
restaurant, no
antibiotics, no flu-like, no tenesmus
Dx - Food borne

Case 18: 26 years old female, 3 days diarrhea, watery not bloody, low grade
fever,
abdominal pain, running nose, muscle pain, no tenesmus, no antibiotics, no
suspicious food


CA 02702720 2010-05-04
Dx - viral gastroenteritis

Appendix C. Running QMR for the diarrhea case
The case description is provided in Appendix A.
Phase 1:
Searching for Diarrhea in QMR yields the following possible terms:
Constipation alternating with diarrhea

diarrhea acute

diarrhea acute recent exposure Hx
diarrhea chronic
diarrhea chronic nocturnal
diarrhea intermittent
diarrhea Profuse watery

We chose "diarrhea acute"

We received the following DD (score ranges shown in parentheses)
(36-65%)

Viral Gastroenteritis
(6-35%)

Campylobacter Intestinal

Staphylococcal Gastroenterities (Food Poisoning)
Cryptosporidial Enteritis

Shigellosis
Salmoella Enterocolities (Non Typhi)

Note that Shigella (the correct diagnosis for this case) is not the top
ranking
diagnosis.

Phase 2:

QMR does not guide the user as to what additional data should be collected
to distinguish among diagnoses. In TiMeDDx, when we know that we have acute
36


CA 02702720 2010-05-04

diarrhea, we acquire from the user the following data: fever (yes), abrupt
presentation (yes), nausea/vomitting (yes), mucus (no), ingestion of
suspicious food
(no), and arthritis (no).

Based on this knowledge, we entered additional findings present into QMR:
"vomitting, recent".

We could not find a term for the abrupt presentation

The following DD was obtained (scores shown in parentheses):
Staphylococcal Scarlet Fever (Toxic Shock Syndrome) (92)
Viral Gatroenteritis (92)

Alcoholic hepatitis (87)
Campylobacter Enteritis (87)
Appendicitis, acute (86)
Leptospirosis Systemic (86)

Cholelithiasis (85)
Cryptosporidial Enteritis (85)
Peritonitis Acute Generalized (85)
Salmoella Enterocolities (Non Typhi) (85)
Shigellosis (85)

Once again, Shigellosis is not the top scoring diagnoses. Comparing to
phase 2 of the TiMeDDx run in Appendix A, we can see that QMR does not use
abstractions, as used in TiMeDDx (e.g., infectious diarrhea). To compare to
phase 3
of the TiMeDDx run, we ran the TiMeDDx algorithm again. Unlike the run shown
in Appendix A, this time "bloody stools" and "abdominal pain" were not
entered, to
match with the QMR run.

Phase 3 of the TiMeDDx algorithm produced the following diagnoses and scores:
Shigella +8+8=16

Non-shigella Bacterial Diarrhea +average(7,4)=5.5
Parasitic Diarrhea +1

Bacterial, antibiotics-associated Diarrhea (Clostridium) +2-8 = -6
37


CA 02702720 2010-05-04

Toxin-borne-diarrhea -18= -18
Comparing phase 3 of TiMeDDx to the QMR DD set, we can see that
Shigella, which is the diagnosis chosen by TiMeDDx has a score of 85 (of a
maximum of 92) in QMR. Note that the first score in QMR, Toxin-borne diarrhea,

ranks extremely low in TiMeDDx (because it is known that the patient did not
ingest suspicious food) and is eliminated by it.

Using QMR, We can continue the diagnostic process by using QMR's
functions for rule out/in different diagnoses in the DD by looking at what
other
findings are suggested by a hypothesis and what test could be ordered to check
for

existence of that finding. For example, checking for enteropathogenic bacteria
by
doing a feces culture, or checking for existence of cholic or diarrhea profuse
watery.
Appendix D. Running DXplain for the diarrhea case

Entered: Diarrhea, male, age 18-40, duration 2-7 days

DD: there was insufficient information to support this diagnosis, as none of
the diseases were well supported.

Rotarovirus Gastroenteritis
Gastroenteritis, viral

Diverticulitis
Influenza
Diabetes Mellitus, type I
Adverse effects of Medication
Colon, diverticulitos
Food alergy
Gastritis, acute
Adenovirus infection

Note that the correct diagnosis was not in the DD-set
DXplain suggested acquiring the following additional data:
Abdominal tenderness, left lower quadrant

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CA 02702720 2010-05-04

Abdominal pain, left lower quadrant
Glycosuria

Ketoacidosis, diabetic

We added "Abdominal pain, left lower quadrant"

The following DD, once again did not include the correct diagnosis. There
was enough information entered to conclude just the top diagnosis on the list.
Diverticulitis

Colon, diverticulitis
Irritable bowl movement
Diabetes Mellitus, type I

Adverse effects of Medication
Food allergy

Gastritis, acute
Gastritis, viral
Influenza

Dxplain lets the user refine the finding of diarrhea before running the
diagnosis algorithm. We picked "bloody diarrhea", obtaining the following DD.
There was enough information entered to conclude just the top diagnosis on the
common diagnosis list and none of the rare diseases. This time, the correct
diagnosis was the 4th item on the rare diseases list.

Common diseases:
Cholitis, ulceritive
Colon, diverticulitis
Camphylobacter enteritis
Food poisoning, Salmonella
Enterocolitis, pseudomembraneous acute
Colon carcinoma

Chron's disease
Food allergy

Rotarovirus Gastroenteritis

39


CA 02702720 2010-05-04
Gastroentritis, viral

Rare diseases:
Amebiasis
Hemolytic uremic syndrome

Mercury poisoning, acute
Shigellosis
Invasive E. Coli

Ricin poisoning

DXplain suggested other findings that could be entered. The relevant one
was blood in stools, gross. Receiving the following DD-set:

Common diseases:
Cholitis, ulceritive
Colon carcinoma
Colon, diverticulitis

Hemorroids
Enterocolitis, pseudomembraneous acute
Camphylobacter enteritis

Food poisoning, Salmonella
Chron's disease

...
Rare diseases:

Lynch syndrome
Amebiasis
Mesenteric vascular insuffeciency, acute
Ricin poisoning

Gardner Syndrome
Hemolytic uremic syndrome
Mercury poisoning, acute
Shigellosis

...



CA 02702720 2010-05-04

This time, the first three diagnoses in the common diseases list and the first
one in the rare diseases list were supported by enough data. The correct
diagnosis
was found in the rare diseases list, this time, at number 8.

In TiMeDDx, when we know that we have acute diarrhea, we acquire from
the user the following data: fever (yes), abrupt presentation (yes),
nausea/vomitting
(yes), bloody stools (yes), mucus (no), ingestion of suspicious food (no), and
arthritis (no).

Based on this knowledge, we entered additional findings present into
DXplain:
"fever", "sudden onset of symptoms", "vomiting", and "stool blood".

The following DD-set, included the correct diagnosis (shigellosis) as the
third diagnosis in the rare diseases list.

Common diseases:
Cholitis, ulceritive
Colon carcinoma

Crohn's disease
Rotarovirus Gastroenteritis
Food poisoning, Salmonella
Camphylobacter enteritis

Enterocolitis, pseudomembraneous acute
Colon, diverticulitis

Gastritis, acute
Intestine obstruction
Rare diseases:
Lynch syndrome
Ricin poisoning
Shigellosis

Vibrio parahaemoliticus infection
Hemolytic uremic syndrome

Mercury poisoning, acute

41


CA 02702720 2010-05-04
Invasive E. Coli

This time, the first four diagnoses in the common diseases list and the first
one in the rare diseases list were supported by enough data. The correct
diagnosis
was not found in the rare or common diseases list.

To conclude, DXplain included the correct rare diagnosis in its DD-set only
when the diagnosis started with a finding that was less abstract than diarrhea
(diarrhea, bloody). The correct diagnosis was not the top-most disease in the
rare
diseases set.


Appendix E. Running GIDEON for the diarrhea case
The case description is provided in Appendix A.
Phase 1:

Diarrhea is entered.

GIDEON produces the following diagnosis:
Shigelloseis (34.3%)

Salmonellosis (24.6%)
Campylobacteriosis (18.5%)
E.Coli Diarrhea (10.3%)

Staphylococcal food poisoning (2%)
Giardiasis (1.9%)

Rotavirus infection (1.9%)

Note that the top diagnosis is the correct one: Shigellosis.

However, GIDEON normally prompts the user to input a few other
parameters (not just one finding): disease onset time and geographical
location

We entered the time frame of a week ago with location being the US, and
received the following DD set:

Campylobacteriosis (38.9%)
E.Coli Diarrhea (21.6%)

Salmonellosis (9.7%)

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CA 02702720 2010-05-04

HIV infection - initial illness (4.7)
Staphylococcal food poisoning (4.1 %)
Shigelloseis (4.1 %)

Rotavirus infection (3.9%)
Giardiasis (3.9%)

Respiratory viruses (miscellaneous) (3.5%)
Amoeba colitis (1.2%)

Now the correct diagnosis is only at #6

The case description contained additional data. Therefore, we entered:
Fever for 7 days, vomiting, bloody stools, abdominal pain
Could not enter "nausea"

Entered finding not present: ingestion of food
Could not enter lack of. mucus, and arthritis.

Now Shigella is identified as the top ranking diagnosis, well-differentiated
from the other diagnoses in the DD-set.

Shigelloseis (83.7%)
E.Coli Diarrhea (4.3%)
Plesiomonas infection (3.5%)
Campylobacteriosis (3.5%)

Salmonellosis (2.9%)

Vibrio parahaemoliticus infection (1%)

But note that GIDEON did not instruct us regarding what data we should be
collecting during the diagnostic process.

43

Representative Drawing