Case-based learning of plans and goal states in medical diagnosis

摘要

We introduce a case-based system, BOLERO, that learns both plans and goal states. The major aim is that of improving the performance of a rule-based diagnosis system by adapting its behavior using the most recent information available about a patient. On the one hand BOLERO gets knowledge from cases in the form of diagnostic plans that are represented as sequences of decision steps. The advantages of this representation include: (1) retrieval and adaptation of parts of plans (steps) appropriate to the current problem state; (2) generation of new plans not previously available in memory; and (3) learning from experience, both from successful or failed plans. On the other hand, since goal states are sets of final diagnosis likelihoods they are not known beforehand, i.e. goal states are not defined and the system has to learn to recognize them. For this reason BOLERO has a case-based method that uses solutions of past cases to recognize a diagnostic state as a goal state of a new planning problem. BOLERO and a rule-based system are integrated into a meta-level architecture in which we emphasize the collaboration of both systems in solving problems. The rule-based system executes the plans generated by BOLERO. As a consequence of the execution of plans, the rule-based system furnishes BOLERO with new information with which BOLERO can generate a new plan to adapt the reasoning process of the rule-based system into correspondence with the recent available data. All the methods have been designed to be useful for medical diagnosis and have been tested in the domain of diagnosing pneumonia.