Efficient Diagnosis of Multiple Disorders Based on a Symptom Clustering Approach

Abstract

Diagnosis of multiple disorders can be made efficient using a new representation and algorithm based on symptom clustering. The symptom clustering approach partitions symptoms into causal groups, in contrast to the existing candidate generation approach, which assembles disorders, or candidates. Symptom clustering achieves efficiency by generating aggregates of candidates rather than individual candidates and by representing them implicitly in a cartesian product form. Search criteria of parsimony, subsumption, and spanning narrow the symptom clustering search space, and a problem-reduction search algorithm explores this space efficiently. Experimental results on a large knowledge base indicate that symptom clustering yields a near-exponential increase in performance compared to candidate generation.