Conditional outlier detection for clinical alerting
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911652934320128 |
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| author | Hauskrecht, Milos Valko, Michal Visweswaran, Shyam Batal, Iyad Clermont, Gilles Cooper, Gregory |
| author_facet | Hauskrecht, Milos Valko, Michal Visweswaran, Shyam Batal, Iyad Clermont, Gilles Cooper, Gregory |
| contents | 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) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05124 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional outlier detection for clinical alerting Hauskrecht, Milos Valko, Michal Visweswaran, Shyam Batal, Iyad Clermont, Gilles Cooper, Gregory Machine Learning Computers and Society 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) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates. |
| title | Conditional outlier detection for clinical alerting |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2605.05124 |