title: Development of a clinical pathways analysis system with adaptive Bayesian nets and data mining techniques.
abstract: The use and development of software in the medical field offers tremendous opportunities for making health care delivery more efficient, more effective, and less error-prone. We discuss and explore the use of clinical pathways analysis with Adaptive Bayesian Networks and Data Mining Techniques to perform such analyses. The computation of "lift" (a measure of completed pathways improvement potential) leads us to optimism regarding the potential for this approach.
authors.affiliation: Brooklyn College, Department of Computer Science, 2900 Bedford Avenue, Brooklyn, NY 11210, USA.
authors.forename: D
authors.initials: D
authors.lastname: Kopec
authors.forename: G
authors.initials: G
authors.lastname: Shagas
authors.forename: D
authors.initials: D
authors.lastname: Reinharth
authors.forename: S
authors.initials: S
authors.lastname: Tamang
mesh_terms: D001499:Bayes Theorem; D019091:Critical Pathways / Q000458:organization & administration*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000295:instrumentation; D016571:Neural Networks, Computer; D012985:Software Design