Development of medical expert systems with fuzzy concepts in a PC environment.

by So Yuen Tai.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.Bibliography: leaves [144]-[146].ACKNOWLEDGEMENTSTABLE OF CONTENTS --- p.T.1ABSTRACTChapter 1. --- INTRODUCTION --- p.1.1Chapter 1.1 --- Inexact Knowledge in Medical Expert Systems --- p.1.1Chapter 1.2 --- Fuzzy Expert System Shells --- p.1.2Chapter 1.2.1 --- SPII-2 --- p.1.3Chapter 1.2.2 --- Fuzzy Expert System Shell for Decision Support System --- p.1.4Chapter 1.3 --- Medical Expert Systems --- p.1.6Chapter 1.3.1 --- EXPERT --- p.1.6Chapter 1.3.2 --- DIABETO --- p.1.8Chapter 1.4 --- Impact from Micro-computer --- p.1.10Chapter 1.5 --- Approach --- p.1.11Chapter 2. --- SYSTEM Z-ll --- p.2.1Chapter 2.1 --- General Description --- p.2.1Chapter 2.2 --- Main Features --- p.2.2Chapter 2.2.1 --- Fuzzy Concepts --- p.2.2Chapter 2.2.2 --- Fuzzy Certainty --- p.2.3Chapter 2.2.3 --- Fuzzy Comparison --- p.2.5Chapter 2.2.4 --- Rule Evaluation --- p.2.7Chapter 2.2.5 --- Certainty Factor Propagation --- p.2.9Chapter 2.2.6 --- Linguistic Approximation --- p.2.10Chapter 2.3 --- Limitations and Possible Improvements --- p.2.11Chapter 3. --- A FUZZY EXPERT SYSTEM SHELL (Z-lll) IN PC ENVIRONMENT --- p.3.1Chapter 3.1 --- General Description --- p.3.1Chapter 3.2 --- Programming Environment --- p.3.1Chapter 3.3 --- Main Features and Structure --- p.3.3Chapter 3.3.1 --- Knowledge Acquisition Module --- p.3.5Chapter 3.3.1.1 --- Object Management Module --- p.3.5Chapter 3.3.1.2 --- Rule Management Module --- p.3.6Chapter 3.3.1.3 --- Fuzzy Term Management Module --- p.3.7Chapter 3.3.2 --- Consultation Module --- p.3.8Chapter 3.3.2.1 --- Fuzzy Inference Engine --- p.3.8Chapter 3.3.2.2 --- Review Management Module --- p.3.11Chapter 3.3.2.3 --- Linguistic Approximation Module --- p.3.11Chapter 3.3.3 --- System Properties Management Module --- p.3.13Chapter 3.4 --- Additional Features --- p.3 14Chapter 3.4.1 --- Weights --- p.3.15Chapter 3.4.1.1 --- Fuzzy Weight --- p.3.16Chapter 3.4.1.2 --- Fuzzy Weight Evaluation --- p.3.17Chapter 3.4.1.3 --- Results of Adding Fuzzy Weights --- p.3.21Chapter 3.4.2 --- Fuzzy Matching --- p.3.24Chapter 3.4.2.1 --- Similarity --- p.3.25Chapter 3.4.2.2 --- Evaluation of Similarity measure --- p.3.26Chapter 3.4.3 --- Use of System Threshold --- p.3.30Chapter 3.4.4 --- Use of Threshold Expression --- p.3.33Chapter 3.4.5 --- Playback File --- p.3.35Chapter 3.4.6 --- Database retrieval --- p.3.37Chapter 3.4.7 --- Numeric Variable Objects --- p.3.39Chapter 3.5 --- Implementation Highlights --- p.3.41Chapter 3.5.1 --- Knowledge Base --- p.4.42Chapter 3.5.1.1 --- Fuzzy Type --- p.4.42Chapter 3.5.1.2 --- Objects --- p.3.45Chapter 3.5.1.3 --- Rules --- p.3.49Chapter 3.5.2 --- System Properties --- p.3.53Chapter 3.5.2.1 --- System Menu --- p.3.53Chapter 3.5.2.2 --- Option Menu --- p.3.54Chapter 3.5.3 --- Consultation System --- p.3.55Chapter 3.5.3.1 --- Inference Engine --- p.3.56Chapter 3.5.3.2 --- Review Management --- p.3.60Chapter 3.6 --- Comparison on Z-lll and Z-ll --- p.3.61Chapter 3.6.1 --- Response Time --- p.3.62Chapter 3.6.2 --- Accessibility --- p.3.62Chapter 3.6.3 --- Accommodation of Large Knowledge Base --- p.3.62Chapter 3.6.4 --- User-Friendliness --- p.3.63Chapter 3.7 --- General Comments on Z-lll --- p.3.64Chapter 3.7.1 --- Adaptability --- p.3.64Chapter 3.7.2 --- Adequacy --- p.3.64Chapter 3.7.3 --- Applicability --- p.3.65Chapter 3.7.4 --- Availability --- p.3.65Chapter 4. --- KNOWLEDGE ENGINEERING --- p.4.1Chapter 4.1 --- Techniques used in Knowledge Acquisition --- p.4.1Chapter 4.2 --- Interviewing the Expert --- p.4.2Chapter 4.3 --- Knowledge Representation --- p.4.4Chapter 4.4 --- Development Approach --- p.4.6Chapter 4.5 --- Knowledge Refinement --- p.4.7Chapter 4.6 --- Consistency Check and Completeness Check --- p.4.12Chapter 4.6.1 --- The Consistency and Completeness in a nonfuzzy rule set --- p.4.13Chapter 4.6.1.1 --- Inconsistency in nonfuzzy rule-based system --- p.4.13Chapter 4.6.1.2 --- Incompleteness in nonfuzzy rule-based system --- p.4.18Chapter 4.6.2 --- Consistency Checks in Fuzzy Environment --- p.4.20Chapter 4.6.2.1 --- Affinity --- p.4.21Chapter 4.6.2.2 --- Detection of Inconsistency and Incompleteness in Fuzzy Environment --- p.4.24Chapter 4.6.3 --- Algorithm for Checking Consistency --- p.4.25Chapter 5. --- FUZZY MEDICAL EXPERT SYSTEMS --- p.5.1Chapter 5.1 --- ABVAB --- p.5.1Chapter 5.1.1 --- General Description --- p.5.1Chapter 5.1.2 --- Development of ABVAB --- p.5.2Chapter 5.1.3 --- Computerisation of Database --- p.5.4Chapter 5.1.4 --- Results of ABVAB --- p.5.7Chapter 5.1.5 --- From Minicomputer to PC --- p.5.15Chapter 5.2 --- INDUCE36 --- p.5.17Chapter 5.2.1 --- General Description --- p.5.17Chapter 5.2.2 --- Verification of INDUCE36 --- p.5.18Chapter 5.2.3 --- Results --- p.5.19Chapter 5.3 --- ESROM --- p.5.21Chapter 5.3.1 --- General Description --- p.5.21Chapter 5.3.2 --- Multi-layer Medical Expert System --- p.5.22Chapter 5.3.3 --- Results --- p.5.25Chapter 6. --- CONCLUSION --- p.6.1REFERENCES --- p.R.1APPENDIX I --- p.A.1APPENDIX II --- p.A.2APPENDIX III --- p.A.3APPENDIX IV --- p.A.1

Epistemic virtues, metavirtues, and computational complexity

I argue that considerations about computational complexity show that all finite agents need characteristics like those that have been called epistemic virtues. The necessity of these virtues follows in part from the nonexistence of shortcuts, or efficient ways of finding shortcuts, to cognitively expensive routines. It follows that agents must possess the capacities – metavirtues –of developing in advance the cognitive virtues they will need when time and memory are at a premium