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Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System By: Karl Boegl, Klaus-Peter Adlassnig, Yoichi Hayashi,

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Presentation on theme: "Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System By: Karl Boegl, Klaus-Peter Adlassnig, Yoichi Hayashi,"— Presentation transcript:

1 Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System By: Karl Boegl, Klaus-Peter Adlassnig, Yoichi Hayashi, Thomas Rothenfluh, and Harald Leitich

2 MedFrame/CADIAG-IV - The predecessor: CADIAG-II - Consultation System - New Implementation - Generalized Domain Model - Fuzzy Dice

3 Definitions Fuzzy Set Theory Fuzzy Logic Fuzzy Control

4 Rule-Based Fuzzy Logic System Rules antecedent consequent Fuzzifier inference engine output processor

5 Fuzzy Logic Example tall(x) = { 0, if height(x) 7 ft. } Membership Function 1.0 + +------------------- | / 0.5 + / | / 0.0 +-------------+-----+------------------- | | 5.0 7.0 height, ft. -> Person Height degree of tallness -------------------------------------- Billy 3' 2" 0.00 Yoke 5' 5" 0.21 Drew 5' 9" 0.38 Erik 5' 10" 0.42 Mark 6' 1" 0.54 Kareem 7' 2" 1.00

6 Logic Operations in Fuzzy Logic truth (not x) = 1.0 - truth (x) truth (x and y) = minimum (truth(x), truth(y)) truth (x or y) = maximum (truth(x), truth(y))

7 Another Example old (x) = { 0, if age(x) < 18 yr. (age(x)-18 yr.)/42 yr., if 18 yr. 60 yr. } a = X is TALL and X is OLD b = X is TALL or X is OLD c = not (X is TALL) height age X is TALL X is OLD a b c ------------------------------------------------------------------------ 3' 2" 65 0.00 1.00 0.00 1.00 1.00 5' 5" 30 0.21 0.29 0.21 0.29 0.79 5' 9" 27 0.38 0.21 0.21 0.38 0.62 5' 10" 32 0.42 0.33 0.33 0.42 0.58 6' 1" 31 0.54 0.31 0.31 0.54 0.46 7' 2" 45 1.00 0.64 0.64 1.00 0.00 3' 4" 4 0.00 0.00 0.00 0.00 1.00

8 Representation within MedFrame/CADIAG-IV Positive Associations F P Consequent => Antecedent S P Antecedent => Consequent Negative Associations F N -(Consequent) => Antecedent S N Antecedent => -(Consequent)

9 Knowledge Refinements (1 of 2)

10 Knowledge Refinements (2 of 2)

11 Project Successes

12 References Title: Knowledge Acquisition in the Fuzzy Knowledge Representation Framework of a Medical Consultation System By: Karl Boegl, Klaus-Peter Adlassnig, Yoichi Hayashi, Thomas Rothenfluh, Harald Leitich Copyright: 2001 Title: The MedFrame Project http://medexpert.imc.akh-wien.ac.at/MedFrame/ By: Dieter Kopecky Copyright: 2000 Title: Medical Expert Systems http://www.computer.privateweb.at/judith/name_3.htm By: Judith Federhofer Copyright: 2002 (More)

13 References (continued) Title: Systematized Nomenclature of Medicine http://www.snomed.org By: College of American Pathologists Copyright: 2001-2003 http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq/html Mendel, Jerry. “Uncertainty in Fuzzy Logic Systems.” University of Southern California.


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