THE INTERPRETATION OF TIME-VARYING DATA WITH DIAMON-1

Applying the methods of Artificial Intelligence to clinical monitoring requires some kind of signal-to-symbol conversion as a prior step. Su bsequent processing of the derived symbolic information must also be s ensitive to history and development, as the failure to address tempora l relationships between findings invariably leads to inferior results. DIAMON-1, a framework for the design of diagnostic monitors, provides two methods for the interpretation of time-varying data: one for the detection of trends based on classes of courses, and one for the track ing of disease histories modelled through deterministic automata. Both methods make use of fuzzy set theory, taking account of the elasticit y of medical categories and allowing discrete disease models to mirror the patient's continuous progression through the stages of illness.