Outlier detection for patient monitoring and 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_ | 1866910205320626176 |
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| author | Hauskrecht, Miloš Batal, Iyad Valko, Michal Visweswaran, Shyam Cooper, Gregory F. Clermont, Gilles |
| author_facet | Hauskrecht, Miloš Batal, Iyad Valko, Michal Visweswaran, Shyam Cooper, Gregory F. Clermont, Gilles |
| contents | 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). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\% to 66\% for a variety of patient-management actions, with 66\% corresponding to the strongest outliers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08955 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Outlier detection for patient monitoring and alerting Hauskrecht, Miloš Batal, Iyad Valko, Michal Visweswaran, Shyam Cooper, Gregory F. Clermont, Gilles Machine Learning 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). Our hypothesis is that a patient-management decision that is unusual with respect to past patient care may be due to an error and that it is worthwhile to generate an alert if such a decision is encountered. We evaluate this hypothesis using data obtained from EHRs of 4486 post-cardiac surgical patients and a subset of 222 alerts generated from the data. We base the evaluation on the opinions of a panel of experts. The results of the study support our hypothesis that the outlier-based alerting can lead to promising true alert rates. We observed true alert rates that ranged from 25\% to 66\% for a variety of patient-management actions, with 66\% corresponding to the strongest outliers. |
| title | Outlier detection for patient monitoring and alerting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.08955 |