Evidence-based anomaly detection in clinical domains
Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory Cooper
Abstract
Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic anomaly detection methods that let us evaluate management decisions for a specific patient and identify those decisions that are highly unusual with respect to patients with the same or similar condition. The statistics used in this detection are derived from probabilistic models such as Bayesian networks that are learned from a database of past patient cases. We apply our methods to the problem of identifying unusual patient-management decisions in post-surgical cardiac patients.
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Classified with taxonomy v2 on Wed, 2 Sept 2026.