Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting

Michal Valko 1 Hamed Valizadegan 2 Branislav Kveton 3 Gregory Cooper 4 Milos Hauskrecht 2
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 Department of Computer Science - University of Pittsburgh
3 Technicolor [Cesson Sévigné]
4 PITT - University of Pittsburgh [Pittsburg]
Abstract : Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response, such as the omission of an important lab test. We develop a new non-parametric approach for conditional anomaly detection based on the soft harmonic solution, with which we estimate the confidence of the label to detect anomalous mislabeling. We further regularize the solution to avoid the detection of isolated examples and examples on the boundary of the distribution support. We demonstrate the efficacy of the proposed method in detecting unusual labels on a real-world electronic health record dataset and compare it to several baseline approaches.
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Michal Valko, Hamed Valizadegan, Branislav Kveton, Gregory Cooper, Milos Hauskrecht. Conditional Anomaly Detection Using Soft Harmonic Functions: An Application to Clinical Alerting. The 28th International Conference on Machine Learning Workshop on Machine Learning for Global Challenges, Jun 2011, Seattle, United States. ⟨hal-00642313⟩

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