title: Rule set reduction using augmented decision table and semantic subsumption techniques: application to cholesterol guidelines.
abstract: Clinical practice guidelines must comprehensively address all logically possible situations, but this completeness may result in sizable and cumbersome rule sets. We applied rule set reduction techniques to a 576-rule set regarding recommendations for medication treatment of hypercholesterolemia. Using decision tables augmented with information regarding test costs and rule application frequencies, we sorted the rule sets prior to identifying irrelevant tests and eliminating unnecessary rules. Alternatively, we examined the semantic relationships among risk factors in hypercholesterolemia and applied a subsumption technique to reduce the rule set. Both methodologies resulted in substantial rule set compression (mean, 48-70%). Subsumption techniques proved superior for compacting a large rule set based on risk factors.
authors: Decision Systems Group, Harvard Medical School, Brigham and Women's Hospital, Boston, MA.
authors: R N
authors: RN
authors: Shiffman
authors: R A
authors: RA
authors: Greenes
mesh_terms: D000328:Adult; D000368:Aged; D004358:Drug Therapy / Q000191:economics; D004360:Drug Therapy, Computer-Assisted; D005260:Female; D006801:Humans; D006937:Hypercholesterolemia / Q000188:drug therapy*; D000960:Hypolipidemic Agents / Q000627:therapeutic use; D008297:Male; D008875:Middle Aged; D017410:Practice Guidelines as Topic; D012307:Risk Factors; D012660:Semantics
chemical_list: D000960:Hypolipidemic Agents