Outlier detection for patient monitoring and alerting
Authors: Miloš Hauskrecht, Iyad Batal, Michal Valko, Shyam Visweswaran, Gregory F. Cooper, Gilles Clermont
Organizations: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA · The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA · Inria Lille-Nord Europe, equipe SequeL, Lille, France · Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA · CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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.