most citedOutlier detection for patient monitoring and alerting

141 citations · 224 across the 3 of their papers we have counts for

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cs.LG2026141 cited

Outlier detection for patient monitoring and alerting

Miloš Hauskrecht, Iyad Batal, Michal Valko +3

We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). O…

cs.LG202648 cited

Conditional outlier detection for clinical alerting

Milos Hauskrecht, Michal Valko, Shyam Visweswaran +3

We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) sys…

cs.LG202635 cited

Evidence-based anomaly detection in clinical domains

Milos Hauskrecht, Michal Valko, Branislav Kveton +2

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…

cs.LG20262 cited

Conditional anomaly detection using soft harmonic functions: An application to clinical alerting

Michal Valko, Hamed Valizadegan, Branislav Kveton +2

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 da…

cs.LG202621 cited

Conditional anomaly detection with soft harmonic functions

Michal Valko, Branislav Kveton, Hamed Valizadegan +2

In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label. We develop a new non-paramet…