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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


Difficulty of theGeneralpractitioners toremain up to date


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Introduction



Access tospecialist is limited


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Introduction




Intolerantpatients


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Introduction




Intolerantpatients


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Introduction



Misdiagnosis


Intolerantpatients


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Previous work


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Previous work

Non-Generic/Specific expert systems


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Previous work


Decision Makers vs. Decision Supporters


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Previous work


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


SubjectiveAnalysis


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Classification


SubjectiveAnalysis

Hypothesisreduction


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Classification


SubjectiveAnalysis

Hypothesisreduction


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Classification


SubjectiveAnalysis

Hypothesisreduction


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Classification


SubjectiveAnalysis

Hypothesisreduction

Hypotheses


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Classification


SubjectiveAnalysis

Hypothesisreduction

Hypotheses


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Classification


SubjectiveAnalysis

Hypothesisreduction

HypothesesObjectiveAnalysis


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Classification


SubjectiveAnalysis

Hypothesisreduction



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Classification


SubjectiveAnalysis

Hypothesisreduction


Probablediagnosis list


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Classification


SubjectiveAnalysis

Hypothesisreduction


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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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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