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.
Complete list of metadatas

Cited literature [7 references]  Display  Hide  Download

Contributor : Michal Valko <>
Submitted on : Monday, November 21, 2011 - 5:31:18 PM
Last modification on : Thursday, August 22, 2019 - 12:10:38 PM
Long-term archiving on : Wednesday, February 22, 2012 - 2:31:23 AM


Files produced by the author(s)


  • HAL Id : hal-00643401, version 1
  • PUBMED : 18693850



Milos Hauskrecht, Michal Valko, Branislav Kveton, Shyam Visweswaran, Gregory Cooper. Evidence-based Anomaly Detection in Clinical Domains. Annual American Medical Informatics Association Symposium, 2007, Chicago, United States. pp.319--324. ⟨hal-00643401⟩



Record views


Files downloads