title: Finding temporal patterns--a set-based approach.
abstract: We created an inference engine and query language for expressing temporal patterns in data. The patterns are represented by using temporally-ordered sets of data objects. Patterns are elaborated by reference to new objects inferred from original data, and by interlocking temporal and other relationships among sets of these objects. We found the tools well-suited to define scenarios of events that are evidence of inappropriate use of prescription drugs, using Medicaid administrative data that describe medical events. The tools' usefulness in research might be considerably more general.
authors.affiliation: Department of Preventive Medicine and Biometrics, University of Colorado Health Sciences Center, Denver 80262.
authors.forename: T D
authors.initials: TD
authors.lastname: Wade
authors.forename: P J
authors.initials: PJ
authors.lastname: Byrns
authors.forename: J F
authors.initials: JF
authors.lastname: Steiner
authors.forename: J
authors.initials: J
authors.lastname: Bondy
mesh_terms: D001185:Artificial Intelligence; D003628:Database Management Systems; D011307:Drug Prescriptions; D005103:Expert Systems; D006801:Humans; D007256:Information Systems; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D011996:Records; D012985:Software Design; D013997:Time Factors