A hybrid case-based system in clinical diagnosis and treatment

Computer-aided decision support systems play an increasingly important role in clinical diagnosis and treatment. However, they are difficult to build for domains where the domain theory is weak and where different experts differ in diagnosis. Stress diagnosis and treatment is an example of such a domain. This paper explores several artificial intelligence methods and techniques and in particular case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic to enable a more reliable diagnosis and treatment of stress. The proposed hybrid case-based approach has been validated by implementing a prototype in close collaboration with leading experts in stress diagnosis. The obtained sensitivity, specificity and overall accuracy compared to an expert are 92%, 86% and 88% respectively.