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Title Event Discovery in Medical Time-Series Data
Author(s) Tsien, Chiristine L.
Source Proceedings of the AMIA 2000 Annual Symposium, Pages 858-862
ISBN 1-56053-480-X
Publisher AMIA
Publication Date November, 2000
Abstract Vast amounts of clinical information are generated daily on patients in the health care setting. Increasingly, this information is collected and stored for its potential utility in advancing health care. Knowledge-based systems, for example, might be able to apply rules to the collected data to determine whether a patient has a certain condition. Often, however, the underlying knowledge needed to write such rules is not well understood. How could these clinical data be useful then? Use of machine learning is one answer. We present a pipeline for discovering the knowledge needed for event detection in medical time-series data. We demonstrate how this process can be applied in the development of intelligent patient monitoring for the intensive care unit (ICU). Specifically, we develop a system for detecting "true alarm" situations in the ICU, where currently as many as 86% of bedside monitor alarms are false. [Sample data in this paper was from a neonatal ICU.]

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