MEDUSA: A Fuzzy Expert System for Medical Diagnosis of Acute Abdominal Pain

Abstract:

Even today, the diagnosis of acute abdominal pain represents a serious clinical problem. The medical knowledge in this field is characterized by uncertainty, imprecision and vagueness. This situation lends itself especially to be solved by the application of fuzzy logic. A fuzzy logic-based expert system for diagnostic decision support is presented (MEDUSA). The representation and application of uncertain and imprecise knowledge is realized by fuzzy sets and fuzzy relations. The hybrid concept of the system enables the integration of rulebased, heuristic and casebased reasoning on the basis of imprecise information. The central idea of the integration is to use casebased reasoning for the management of special cases, and rulebased reasoning for the representation of normal cases. The heuristic principle is ideally suited for making uncertain, hypothetical inferences on the basis of fuzzy data and fuzzy relations.

Keywords:

Acute Abdominal Pain - Decision Support - Fuzzy Logic - Hybrid Expert Systems

• REFERENCES

• 1 Adams ID, De Dombal FT. et al. Computer aided diagnosis of acute abdominal pain: A multicentre study. Br Med J 1986; 293: 800-8.
• 2 Clifford PC, Chan M, Hewett DJ. The acute abdomen: Management with microcomputer aid. Ann R Coll Surg Engl 1986; 68: 182-6.
• 3 Hancock DM, De Dombal F. et al. Computer aided diagnosis of acute abdominal pain. The practical impact of a “theoretical” exercise. Theor Surg 1987; 02: 99-105.
• 4 Scarlett PY. et al. Computer aided diagnosis of acute abdominal pain at Middlesborough general hospital. Ann R Coll Surg 1986; 68: 179-84.
• 5 Hoffmann J, Rasmussen O. Aids in the diagnosis of acute appendicitis. Br J Surg 1989; 76: 774-9.
• 6 Sutton GC. How accurate is computeraided diagnosis?. Lancet 1989; 48: 905-8.
• 7 Ohmann C. et al. Akuter Bauchschmerzstandardisierte Befundung als Diagnoseunterstiitzung - Ergebnisse einer prospektiven multizentrischen Interventionsstudie und Testung eines computergestutzten Diagnosesystems. Chirurg 1992; 63: 113-23.
• 8 De Dombal FT. et al. Computeraided diagnosis of acute abdominal pain. Br Med J 1972; 239: 9-11.
• 9 Ohmann C, Kraemer M. Evaluierung von Entscheidungsunterstutzungssystemen bei der Diagnose von akuten Bauchschmerzen Eine Analyse publizierter Systeme. Biometrie und Informatik in Medizin und Biologie 1992; 23: 107-11.
• 10 Zadeh LA. Fuzzy sets. Information and Control 1965; 11: 199-217.
• 12 Zadeh LA. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions. IEEE Trans Man Cybern 1985; 15: 754-63.
• 13 Zadeh LA. Fuzzy logic. IEEE Computer 1988; 83-93.
• 14 Sanchez E. Composite of fuzzy relations. In: Gupta MM, Ragade RK, Yager RR. eds. Advances in Fuzzy Set Theory and Applications. Amsterdam: North-Holland Publ Comp; 1979: 421-36.
• 15 Sanchez E. Medical diagnosis and composite fuzzy relations. In: Gupta MM, Ragade RK, Yager RR. eds. Advances in Fuzzy Set Theory and Applications. Amsterdam: North-Holland Publ Comp; 1979: 437-45.
• 16 Adlassnig KP. Fuzzy set theory in medical diagnosis. IEEE Trans Systems, Man Cybern. 1986: 16.
• 17 Adlassnig KP. et al. Present state of the medical expert system CADIAG-2. Meth Inform Med 1985; 24: 13-20.
• 18 Adlassnig KP, Scheithauer W, Kolarz G. Fuzzy medical diagnosis in a hospital. In: Prade H, Negoita CV. eds. Fuzzy Logic in Knowledge Engineering. Koln: Verlag TUV Rheinland GmbH; 1986
• 19 Adlassnig KP. et al. Medizinische Expertenund Konsultationssysteme. Uberblick und Anwendungsbeispiele. Acta Med Austria. 1987: 14.
• 20 Binaghi E. A fuzzy logic inference module for a rule based system in medical diagnosis. Expert Systems 1990; 07: 134-41.
• 21 Dutta S, Bonissone PP. Integrating case based and rule based reasoning: The possibilistic connection. In: Bonissone PP, Herion M, Kanal LN, Lemmer JF. eds. Uncertainty in Artificial Intelligence 6. Amsterdam: Elsevier Science Publ; 1991
• 22 Fathi M. et al. A medical knowledgebased system for cerebral tumor diagnosis. In: International Computer Conference in Medicine 1991; 93-7.
• 23 Fathi M. et al. Ein Konzept fur Fuzzy Logik in Online-Diagnoseystemen. In: Technische Anwendungen von Fuzzy Systemen. Vortrage der VDE-Fachtagung 1992; 93-100.
• 24 Fathi M. Fuzzy expert system approaches. In: 12th World Congress IF AC 93 1993; 2-6.
• 25 Godo LI, Lopez de Mantaras R, Sierra C. Managing linguistically expressed uncertainty in MILORD application to medical diagnosis. In: 7th International Workshop on Expert Systems and their Applications. Avignon, France; 1987: 115-9.
• 26 Holzmann CA, Perez CA, Rosselot E. A fuzzy model for medical diagnosis. Medical Progress through Technology 1988; 13: 171-8.
• 27 Leung KS, Felix W, Lam W. The development of an expert computer system on medical consultatio. Int J Biomed Comput 1988; 23: 265-78.
• 28 Soula G. et al. PROTIS: A fuzzy expert system with medical applications. In: Prade M, Negoita CV. eds. Fuzzy Logic in Knowledge Engineering. Verlag TUV Rheinland GmbH. 1986
• 29 Golding AR, Rosenbloom PS. Improving rulebased systems through casebased reasoning. In: Ninth National Conference on Artificial Intelligence. AAAIU 1991; 22-7.
• 30 Althoff KD. et al. Fallbasiertes SchlieBen in Expertensystemen: Welche Rolle spielen Falle fur wissensbasierte Systeme?. Kiinstliche Intelligenz 1991; 04: 69-72.
• 31 Althoff KD. et al. Die Lernkomponente der MOLTKE-Werkbank zur Diagnose technischer Systeme. Ktinstliche Intelligenz 1991; 01: 7.
• 32 Miller RA. et al. INTERNIST-1, an experimental computerbased diagnostic consultant for general internal medicine. In: Short-liffe EH, Clance WJ. eds. Readings in Medical Artificial Intelligence. New York: Addison-Wesley; 1984: 190-209.
• 33 Puppe F. Assoziatives diagnostisches Problemlosen mit dem Expertensystemshell MED2 (Dissertation). Kaiserslautern: University; 1986