Fuzzy Logic: from Mathematics to medical Applications

01.04.2008 - 30.11.2012
The last decades have witnessed a considerable development of rule-based systems in medicine with the purpose of assisting physicians in medical decision-making. CADIAG (Computer-Assisted DIAGnosis) 1-4 are successful systems of this kind developed by Prof. Adlassnig's group and integrated into the medical information system of the Vienna General Hospital (AKH). The aim of the systems is to propose diagnoses (in the field of internal medicine) based on patient's given symptoms, signs, and test results. CADIAG-1 is based on a symbolic logic representation of medical relationships (rules) between symptoms, signs, or findings and diseases. A simple and elegant formalization of CADIAG-1's rules into the monadic fragment of classical logic without nested quantifies allowed for consistency checking of the knowledge base (and the detection of 17 inconsistencies out of the 50.000 rules of CADIAG-1). Precise and definite information about real world objects is however difficult to obtain, and, in the realm of medicine, such information is not always accessible to doctors for diagnosis and treatment. To process vague information, the successor systems CADIAG 2 and 4 were based on fuzzy set theory ("fuzzy logic", using Zadeh's terminology) and the binary relations (IF-THEN rules) evaluated using the min/max approach. This evaluation however does not take into account several independent rules confirming the same diagnosis with an equal weight. Moreover, not being based on formal logics, the resulting systems do not lend themselves to rigorous checking (e.g. consistency) and it is not clear whether their inferential mechanism can be exported to other medical areas. Using the potential of our theoretical research in t-norms based logics (that have been recognized to be the logical counterpart of many inferential mechanisms of "fuzzy logic") and automated deduction we aim to solve the above drawbacks. In particular, we will use symbolic logic to (i) perform a formal consistency check of CADIAG 2-4's rules, (ii) formally justify the choice of the operators (t-norms) and the way of combining the rules of the systems and (iii) allow for computing satisfactory results in presence of incomplete information. The potential of fuzzy description logics to describe medical ontologies in these systems will be also explored. Our starting point will be the formalization of CADIAG-1's rules in first-order classical logic.






  • WWTF Wiener Wissenschafts-, Forschu und Technologiefonds (National) Wiener Wissenschafts-, Forschungs- und Technologiefonds (WWTF) Fördergeber Typ Bundesland Wien


  • Information and Communication Technology


fuzzy logicfuzzy logic
expert systemsexpert systems

Externe Partner_innen

  • Institut für Informationssysteme
  • Universität Wien