Evidence-based anomaly detection in clinical domains
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913094326812672 |
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| author | Hauskrecht, Milos Valko, Michal Kveton, Branislav Visweswaran, Shyam Cooper, Gregory |
| author_facet | Hauskrecht, Milos Valko, Michal Kveton, Branislav Visweswaran, Shyam Cooper, Gregory |
| contents | 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 apply our methods to the problem of identifying unusual patient-management decisions in post-surgical cardiac patients. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evidence-based anomaly detection in clinical domains Hauskrecht, Milos Valko, Michal Kveton, Branislav Visweswaran, Shyam Cooper, Gregory Machine Learning 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 apply our methods to the problem of identifying unusual patient-management decisions in post-surgical cardiac patients. |
| title | Evidence-based anomaly detection in clinical domains |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.04664 |