title: RASTA: a distributed temporal abstraction system to facilitate knowledge-driven monitoring of clinical databases.
abstract: The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.
authors.affiliation: Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA 94305-5479, USA. moconnor@smi.stanford.edu
authors.forename: M J
authors.initials: MJ
authors.lastname: O'Connor
authors.forename: W E
authors.initials: WE
authors.lastname: Grosso
authors.forename: S W
authors.initials: SW
authors.lastname: Tu
authors.forename: M A
authors.initials: MA
authors.lastname: Musen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D011381:Programming Languages; D013995:Time