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Development and Evaluation of Predictive Alerts for Hemodynamic Instability in ICU Patients

By Larry J. Eshelman, K. P. Lee, Joseph J. Frassica, Wei Zong, Larry Nielsen and Mohammed Saeed


This paper describes an algorithm for identifying ICU patients that are likely to become hemodynamically unstable. The algorithm consists of a set of rules that trigger alerts. Unlike most existing ICU alert mechanisms, it uses data from multiple sources and is often able to identify unstable patients earlier and with more accuracy than alerts based on a single threshold. The rules were generated using a machine learning technique and were tested on retrospective data in the MIMIC II ICU database, yielding a specificity of approximately 0.9 and a sensitivity of 0.6

Topics: Articles
Publisher: American Medical Informatics Association
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Provided by: PubMed Central
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