Abstract:
The central purpose of artificial intelligence applied to medicine is to develop models
for diagnosis and therapy planning at the knowledge level, in the Newell sense, and
software environments to facilitate the reduction of these models to the symbol level.
The usual methodology (KADS, CommonKADS, GAMES, HELIOS, Protégé, etc.) has been to
develop libraries of generic tasks and resuable problem-solving methods with explicit
ontologies. The principal problem which clinicians have with these methodological
developments concerns the diversity and complexity of new terms whose meaning is not
sufficiently clear, precise, unambiguous and consensual for them to be accessible
in the daily clinical environment. As a contribution to the solution of this problem,
we develop in this article the conjecture that one inference structure is enough to
describe the set of analysis tasks associated with medical diagnoses. To this end,
we first propose a modification of the systematic diagnostic inference scheme to obtain
an analysis generic task and then compare it with the monitoring and the heuristic
classification task inference schemes using as comparison criteria the compatibility
of domain roles (data structures), the similarity in the inferences, and the commonality
in the set of assumptions which underlie the functionally equivalent models. The equivalences
proposed are illustrated with several examples. Note that though our ongoing work
aims to simplify the methodology and to increase the precision of the terms used,
the proposal presented here should be viewed more in the nature of a conjecture.
Keywords
Medical Diagnosis - Inference Structures - Unification - KADS
