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Outlier detection for patient monitoring and alerting

Milos Hauskrecht 1 Iyad Batal 1 Michal Valko 1, 2 Shyam Visweswaran 3 Gregory F Cooper 3 Gilles Clermont 4
2 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25% to 66% for a variety of patient-management actions, with 66% corresponding to the strongest outliers.
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Contributor : Michal Valko Connect in order to contact the contributor
Submitted on : Monday, October 15, 2012 - 7:02:23 PM
Last modification on : Thursday, January 20, 2022 - 4:12:30 PM

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Milos Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F Cooper, et al.. Outlier detection for patient monitoring and alerting. Journal of Biomedical Informatics, Elsevier, 2013, 46, pp.47-55. ⟨10.1016/j.jbi.2012.08.004⟩. ⟨hal-00742097⟩



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