title: Knowledge acquisition for temporal abstraction.
abstract: Temporal abstraction is the task of detecting relevant patterns in data over time. The knowledge-based temporal-abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific temporal-abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire temporal knowledge and to use that knowledge to perform automated temporal reasoning.
authors.affiliation: Section on Medical Informatics, Stanford University School of Medicine, CA 94305-5479, USA.
authors.forename: A
authors.initials: A
authors.lastname: Stein
authors.forename: M A
authors.initials: MA
authors.lastname: Musen
authors.forename: Y
authors.initials: Y
authors.lastname: Shahar
mesh_terms: D001185:Artificial Intelligence; D003196:Computer Graphics; D012984:Software; D012985:Software Design; D013995:Time