A Diagnostic Process Extended in Time as a Fuzzy Model

• The paper refers to earlier results obtained by the authors and constitutes their essential complement and extension by introducing to a diagnostic model the assumption that the decision concerning the diagnosis is based on observations of symptoms carried out repeatedly, by stages, which may have effect in a change of these symptoms in increasing time. The model concerns the observations of symptoms at an individual patient at a time interval. The changes of the symptoms give some additional information, sometimes very important in the diagnostic process when the clinical picture of a patient in a certain interval of time differs from that one which has been received from the beginning of the disease. It may occur that the change in the intensity of a symptom decides an acceptance of another diagnosis after some time when the patient does not feel better. The aim is to fix an optimal diagnosis on the basis of clinical symptoms typical of several morbid units with respect to the changes of these symptoms in time. In order to solve such a posed problem the authors apply the method of fuzzy relation equations, which are modelled by means of logical laws and the rules of inference. Moreover, in the final decision concerning the choice of a proper diagnosis, a normalized Euclidean distance is introduced as a measure between a real decision and an ”ideal” decision. A simple example presents the practical action of the method to show its relevance to a possible user.

Subject headings

NATURVETENSKAP  -- Matematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)

Keyword

Extensions of fuzzy relation equations

logical laws

a normalized Euclidean distance

observations in time

an optimal diagnostic model

Publication and Content Type

vet (subject category)

kap (subject category)