Multivariate Conditional Anomaly Detection and Its Clinical Application

This paper overviews the background, goals, past achievements and future directions of our research that aims to build a multivariate conditional anomaly detection framework for the clinical application. Background and Goals We humans are prone to error. Despite startling advances in medicine, the occurrence of medical errors remains a persistent and critical problem. Although various computeraided monitoring devices support medical practices to prevent errors, because those tools are primarily knowledgebased built by clinical experts, they are expensive and their clinical coverage is incomplete. We develop a new detection framework that identifies statistically anomalous patient care patterns based on past clinical information stored in electronic health record (EHR) systems. Our hypothesis is that the detection of anomalies in patient care patterns corresponds to identifying cases that need medical attention for reconsideration. Typical anomaly detection methods, however, simply at...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Charmgil Hong, Milos Hauskrecht
• Keywords: AAAI 2015, Intelligent Agents