title: An algebraic approach to detect logical inconsistencies in medical appropriateness criteria.
abstract: In this paper, we present a computerized approach to detect inconsistencies in medical knowledge bases. The method has been applied to a set of medical appropriateness criteria developed for the review of coronary artery disease management. One of the main problems associated to medical appropriateness criteria is to detect logical inconsistencies in the criteria set, a process often manually carried out by health services specialists. In our approach, appropriateness criteria are automatically translated to rules containing propositional variables, using three-valued Łukasiewicz's logic augmented with modal operators to manage uncertainty. The method assigns a polynomial to each of the rules, integrity constraints, and facts from the rule-based set. This rule set is then checked for inconsistencies. The problem of determining if a formula is a tautological consequence of a set of formulae is reduced by our method into an ideal membership problem in computer algebra. Finally, the set of medical appropriateness criteria is represented in a flowchart format that can be disseminated and remotely accessed over Internet, and can be prospectively used for patient care and management. The method reported in this paper can be applied to other knowledge bases represented by means of IF-THEN rules.
authors.affiliation: Biomedical Informatics Group, School of Computer Science, Universidad Politécnica de Madrid. Campus de Montegancedo S/N, 28660 Boadilla del Monte, Madrid, Spain. mgarcia@infomed.dia.fi.upm.es
authors.forename: Miguel
authors.initials: M
authors.lastname: García-Remesal
authors.forename: Victor
authors.initials: V
authors.lastname: Maojo
authors.forename: Luis
authors.initials: L
authors.lastname: Laita
authors.forename: Eugenio
authors.initials: E
authors.lastname: Roanes-Lozano
authors.forename: José
authors.initials: J
authors.lastname: Crespo
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D016015:Logistic Models; D016347:Medical Records Systems, Computerized; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013030:Spain; D014584:User-Computer Interface