Conditional anomaly detection methods for patient-management alert systems

Michal Valko 1, * Gregory Cooper 2 Amy Seybert 3 Shyam Visweswaran 4 Melissa Saul 2 Milos Hauskrecht 5
* Auteur correspondant
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 University of Pittsburgh
3 University of Pittsburgh Medical Center
4 Department of Biomedical Informatics
5 Department of Computer Science - University of Pittsburgh
Abstract : Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection framework extends anomaly detection to the problem of identifying anomalous patterns on a subset of attributes in the data. The anomaly always depends (is conditioned) on the value of remaining attributes. The work presented in this paper focuses on instance-based methods for detecting conditional anomalies. The methods rely on the distance metric to identify examples in the dataset that are most critical for detecting the anomaly. We investigate various metrics and metric learning methods to optimize the performance of the instance-based anomaly detection methods. We show the benefits of the instance-based methods on two real-world detection problems: detection of unusual admission decisions for patients with the community-acquired pneumonia and detection of unusual orders of an HPF4 test that is used to confirm Heparin induced thrombocytopenia - a life-threatening condition caused by the Heparin therapy.
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Communication dans un congrès
Workshop on Machine Learning in Health Care Applications in The 25th International Conference on Machine Learning, Jul 2008, Helsinki, Finland
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Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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Michal Valko, Gregory Cooper, Amy Seybert, Shyam Visweswaran, Melissa Saul, et al.. Conditional anomaly detection methods for patient-management alert systems. Workshop on Machine Learning in Health Care Applications in The 25th International Conference on Machine Learning, Jul 2008, Helsinki, Finland. 〈hal-00643221〉

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