Outlier detection for patient monitoring and alerting

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.

Published at JBI 2013

References in corpus (3)

• Conditional outlier detection for clinical alerting
• Evidence-based anomaly detection in clinical domains
• Feature importance analysis for patient management decisions

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• Out-of-Category Document Identification Using Target-Category Names as Weak Supervision
• Detecting Unusual Input-Output Associations in Multivariate Conditional Data
• Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder