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Methods Inf Med 1985; 24(01): 13-20
DOI: 10.1055/s-0038-1635354
Original Article
Schattauer GmbH

Present State of the Medical Expert System CADIAG-2

Gegenwärtiger Stand des medizinischen Expertensystems CADIAG-2
K.-P. Adlassnig
1   (From the Department of Medical Computer Sciences [Director: Prof. Dr. G. Grabner] and the 2nd Department of Gastroenterology and Hepatology [Director: Prof. Dr. G. Grabner], University of Vienna, and the Ludwig Boltzmann Institute of Rheumatology and Focal Diseases [Directors: Prof. Dr. F. Endler and Prof. Dr. N. Thumb], Baden, Austria)
,
G. Kolarz
1   (From the Department of Medical Computer Sciences [Director: Prof. Dr. G. Grabner] and the 2nd Department of Gastroenterology and Hepatology [Director: Prof. Dr. G. Grabner], University of Vienna, and the Ludwig Boltzmann Institute of Rheumatology and Focal Diseases [Directors: Prof. Dr. F. Endler and Prof. Dr. N. Thumb], Baden, Austria)
,
W. Scheithauer
1   (From the Department of Medical Computer Sciences [Director: Prof. Dr. G. Grabner] and the 2nd Department of Gastroenterology and Hepatology [Director: Prof. Dr. G. Grabner], University of Vienna, and the Ludwig Boltzmann Institute of Rheumatology and Focal Diseases [Directors: Prof. Dr. F. Endler and Prof. Dr. N. Thumb], Baden, Austria)
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Publication History

Publication Date:
20 February 2018 (online)

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Summary

Uncertainty of knowledge about the patient and about medical relationships is generally accepted and considered to be an inherent concept in medicine. The physician, however, is quite capable of drawing conclusions from this information. Naturally, these conclusions are approximate rather than precise.

Fuzzy set theory provides the possibility of defining imprecise medical entities as fuzzy sets. It offers a linguistic concept with excellent approximation to medical texts. In addition, fuzzy logic presents powerful reasoning methods that can handle approximate inferences. These facts make fuzzy set theory highly suitable for the development of computer-based medical diagnostic systems.

The medical expert system CADIAG-2 provides evidence that fuzzy set theory is a suitable mathematical tool for formalizing medical processes.

CADIAG-2/RHEUMA is being extensively tested on cases from a rheumatological hospital. Results from 327 cases are presented. In 265 cases, i.e. 81%, the clinical diagnosis could be either confirmed (223 cases, i.e. 68.2%) or established as a diagnostic hypothesis (42 cases, i.e. 12.8%).

CADIAG-2/PANCREAS was tested on 47 cases of pancreatic diseases. In 43 cases, i.e. 91.5%, the clinical diagnosis was either confirmed by CADIAG-2 or established as one of the hypotheses with the highest or second highest number of points in a ranked list of hypotheses.

Unscharfe im Wissen über den Patienten und über medizinische Beziehungen wird in der Medizin allgemein akzeptiert und als ihr innewohnend betrachtet. Der Arzt berücksichtigt sie bei seinen täglichen Handlungen und ist in der Lage, logische Schlüsse aus unscharfen Gegebenheiten zu ziehen.

Die Theorie der fuzzy Mengen erlaubt es, unscharfe medizinische Entitäten als fuzzy Mengen zu definieren. Sie bietet weiterhin ein linguistisches Konzept zur Formalisierung sprachlicher Aussagen sowie eine Theorie der unscharfen Logik, die die formale Behandlung unscharfer Schlüsse aus unscharfen Aussagen ermöglicht.

Das medizinische Expertensystem CADIAG-2 zeigt, daß die Theorie der fuzzy Mengen ein geeignetes Werkzeug zur Formalisierung medizinischer Prozesse ist.

CADIAG-2/RHEUMA wird an Fällen eines rheumatologischen Krankenhauses getestet. Die Ergebnisse von 327 Diagnoseläufen werden diskutiert. In 265 Fällen, d. h. 81%, gelang es, die klinische Diagnose entweder zu beweisen (223 Fälle, d. h. 68,2%) oder sie als Diagnosehypothese zu generieren (42 Fälle, d. h. 12,8%).

CADIAG-2/PANCREAS wurde an 47 Fällen einer Universitätsklinik getestet. In 43 Fällen, d. h. 91,5%, konnte die klinische Diagnose entweder bewiesen oder als Diagnosehypothese mit der höchsten oder zweithöchsten Punktzahl im Rahmen einer Rangordnung der Hypothesen generiert werden.

Key-Words:

Medical Expert System CADIAG-2 - Fuzzy Set Theory - Rheumatology - Pancreatic Diseases

Schlüssel-Wörter:

Medizinisches Expertensystem CADIAG-2 - Theorie der fuzzy Mengen - Rheumatologie - Pankreaserkrankungen
 

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