title: Evidence-based anomaly detection in clinical domains.
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 evaluate our methods on the problem of detection of unusual hospitalization patterns for patients with community acquired pneumonia. The results show very encouraging detection performance with 0.5 precision at 0.53 recall and give us hope that these techniques may provide the basis of intelligent monitoring systems that alert clinicians to the occurrence of unusual events or decisions.
authors.affiliation: Computer Science Department, University of Pittsburgh, PA, USA.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
authors.forename: Michal
authors.initials: M
authors.lastname: Valko
authors.forename: Branislav
authors.initials: B
authors.lastname: Kveton
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
mesh_terms: D001499:Bayes Theorem; D017714:Community-Acquired Infections; D003661:Decision Support Techniques; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010346:Patient Care Management / Q000379:methods*; D011014:Pneumonia; D017010:Reminder Systems