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Generic Fuzzy BayesianExpert System in Cardiology
Hossein Rahnama, Ryerson University
Dr.Alireza Sadeghian, Ryerson University
Dr.William Melek, University of Waterloo
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Introduction
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Introduction
Expansion of themedical knowledge
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Introduction
Expansion of themedical knowledge
Difficulty of theGeneralpractitioners toremain up to date
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Introduction
Expansion of themedical knowledge
Difficulty of theGeneralpractitioners toremain up to date
Access tospecialist is limited
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Introduction
Expansion of themedical knowledge
Difficulty of theGeneralpractitioners toremain up to date
Access tospecialist is limited
Intolerantpatients
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Introduction
Expansion of themedical knowledge
Difficulty of theGeneralpractitioners toremain up to date
Access tospecialist is limited
Intolerantpatients
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Introduction
Expansion of themedical knowledge
Difficulty of theGeneralpractitioners toremain up to date
Misdiagnosis
Access tospecialist is limited
Intolerantpatients
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Previous work
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Previous work
Non-Generic/Specific expert systems
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Previous work
Non-Generic/Specific expert systems
Decision Makers vs. Decision Supporters
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Previous work
Non-Generic/Specific expert systems
Decision Makers vs. Decision Supporters
No systematic guidelines for creating ageneric medical system
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Objective
Develop a framework that mimics thereasoning methods of a physician.
Diagnosis Treatment
Target Users:
Physicians and General Practitioner
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Rudiments
Understanding the diagnosis
Researching the patient encounter
Creating a logic from the patients encounter
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Patient Encounter:
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Physicians reasoning model:
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Patients Demography
History Symptoms
Subjective Analysis
Patient
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Subjective Analysis
Symptoms History
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Knowledge for Subjective Analysis
Different techniques can beconsidered for SubjectiveAnalysis:
A scoring system
Bayesian Inference
Fuzzy Logic
Simple scoring system caneffectively represent this stage
Ability to capture requiredinformation without complicatingthe process
History Present symptoms
Smoking Syncope
Hypertension Anxiety
High Stress Chest ache
Obesity Cough, acute
Atherosclerosis Dizziness
Diabetes mellitus Nausea, vomiting
Hyperlipidemia Shortness of breath
Sweating
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Subjective Analysis
The presence of absence of any symptom is presented by one orzero in a ones-row symptom matrix:
Where X1 is the finding or symptom that can be eitherAbsent or Present
The Weight vector of n symptoms is given by a one-column matrix as follows:
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Subjective Analysis
The multiplication of the one-row symptom matrix of every disease
Dsby a one-column weight matrix yields the result of the symptomsscoring of this disease as follows:
Similar forms of equation can be used to asses the history array Hwith a similar weighting system WHfor every history finding:
DH = H.WH
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Subjective Analysis: Creating a threshold
Two separate scores are calculated with two separate thresholds:
Symptom score with threshold of 50
History score with threshold of 30
Simple Rule to every disease rule-base:
IF Symptom-Score >50 AND History-Score >30
Then disease will be in the Probable_Diagnosis_List
Upon obtainment ofWs and Whthe subjective rule base is complete
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Classification
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Classification
Unclassifieddiseases
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Classification
Unclassifieddiseases
SubjectiveAnalysis
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
Hypotheses
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
Hypotheses
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
HypothesesObjectiveAnalysis
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
HypothesesObjectiveAnalysis
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
HypothesesObjectiveAnalysis
Probablediagnosis list
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Classification
Unclassifieddiseases
SubjectiveAnalysis
Hypothesisreduction
HypothesesObjectiveAnalysis
Probablediagnosis list
In Cardiology cases:
10 Disease category Subjective Analysis75 specific final diagnoses Objective Analysis
Cardiac Classifications
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Cardiac Classifications
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Objective Analysis
Using labs and imaging studies to reduce the numberhypotheses
Creation of a probable diagnosis list
Creating data sets for each classified disease
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Objective Analysis
Creating an Acyclic graph for objective scoring
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Objective Analysis
Rule base
Total Test Score
Disease Probability
Test Weights
Disease name
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Objective Tables
Tables in many cases are big
Cannot be used to deal with missing information
A method that can be used to create rules from suchtables
Inability to process missing information
Fuzzy Logic
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Trapezoids membership functions
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Membership functions
Examples:
Number of rules in Aortic Stenosis reduced from 1728 to 20 fuzzy rules
Number of rules in Unstable Angina reduced from 2304 to 24 fuzzy rules
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Probable diagnosis list
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Bayesian Decisions
Decision in the probable diagnosis list
Calculating the risk of the decision
Calculating the error of diagnosis
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Bayesian Approach in Diagnosis
Deciding between the probable diagnosis list:
If the likelihood of two diseases are equivalent
Disease1 :
Disease2:
Prior probabilities
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Observation based on lab result
If is an observation from a lab test:
True state of disease:
True state of disease:
Simple Diagnosis rule based on a shared feature
Posterior
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Deciding between an array of diagnoses
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Dual Category Classification: Deciding between multiplediseases:
Loss in making a
wrong diagnosis
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Diagnosis spaces
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Decision in Cardiomyopathies
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Decision in Cardiomyopathies
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Based on three labs
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Multi Category classification
Case Study 2:
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Case Study 2:
Class: Vulvular Heart DiseasesDiseases: Tricsupid Stenosis and Mitral Regurgitation
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Class: Vulvular Heart DiseasesDiseases: Tricsupid Stenosis and Mitral Regurgitation
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Class: Vulvular Heart DiseasesDiseases: Tricsupid Stenosis and Mitral Regurgitation
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Pulmonary Hypertension vs. Pulmonary EdemaOverlapping
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Restrictive vs. Dilated Cardiomyopathies
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Prinzmetal Angina vs. Unstable Angina
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Overview
True state of diagnosis
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Suggested Architecture
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