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PubMed · 15360780

A knowledge-based time-oriented active database approach for intelligent abstraction, querying and continuous monitoring of clinical data.

Alex Spokoiny·Yuval Shahar
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Abstract

Query and interpretation of time-oriented medical data involves two subtasks: Temporal-reasoning--intelligent analysis of time-oriented data, and temporal-maintenance--effective storage, query, and retrieval of these data. Integration of these tasks into one system, known as temporal-mediator, has been proven to be beneficial to biomedical applications such as monitoring, therapy, quality assessment, visualization and exploration of time-oriented data. One potential problem in existing temporal-mediation approaches is lack of sufficient responsiveness when querying or continuously monitoring the database for complex abstract concepts that are derived from the raw data, especially regarding a large patient group. We propose a new approach: the knowledge-based time-oriented active database, a temporal extension of the active-database concept, and a merger of temporal reasoning and temporal maintenance within a persistent database framework. The approach preserves the efficiency of databases in handling data storage and retrieval, while enabling specification and performance of complex temporal reasoning using an incremental-computation approach. We implemented our approach within the Momentum system. Initial experiments are encouraging; an evaluation is underway

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AlgorithmsArtificial IntelligenceDatabase Management SystemsDatabases as TopicProgramming LanguagesTimeJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, P.H.S.

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Alex Spokoiny, Yuval Shahar. 2004. A knowledge-based time-oriented active database approach for intelligent abstraction, querying and continuous monitoring of clinical data.. https://pubmed.ncbi.nlm.nih.gov/15360780/

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