Modeling uncertainty in clinical diagnosis using fuzzy logic.
This paper describes a fuzzy approach to computer-aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps. A constraint satisfaction method is introduced that uses the temporal uncertainty in symptom durations that may occur with particular diseases. The method results in an estimate of the stage of the disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. A lightweight fuzzy process is described and evaluated in the context of diagnosis of two confusable diseases. The process is based on the idea of an incremental simple additive model for fuzzy sets supporting and negating particular diseases. These are combined to produce an index of support for a particular disease. The process is developed to allow fuzzy symptom information on the intensity and duration of symptoms. Results are presented showing the effectiveness of the method for supporting differential diagnosis.
John
R I
RI
Centre for Computational Intelligence, School of Computing, De Montfort University, Leicester, UK. rij@dmu.ac.uk
Innocent
P R
PR
D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis*; D008954:Models, Biological; D015233:Models, Statistical