@article{hauskrecht2012outlier,
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.},
author = {Hauskrecht, Milos and Batal, Iyad and Valko, Michal and Visweswaran, Shyam and Cooper, Gregory F and Clermont, Gilles},
doi = {10.1016/j.jbi.2012.08.004},
issn = {1532-0464},
journal = {Journal of Biomedical Informatics},
keywords = {Clinical alerting,Conditional outlier detection,Machine learning,Medical errors},
month = feb,
number = {1},
pages = {47--55},
title = {{Outlier detection for patient monitoring and alerting}},
url = {http://www.sciencedirect.com/science/article/pii/S1532046412001281},
volume = {46},
year = {2013}
}