A Knowledge-Based Time-Oriented Active Database Approach for Intelligent Abstraction, Querying and Continuous Monitoring of Clinical Data

摘要

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.