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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2507.22798 |
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| _version_ | 1866916871326924800 |
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| author | Burkhart, Michael C. Ramadan, Bashar Solo, Luke Parker, William F. Beaulieu-Jones, Brett K. |
| author_facet | Burkhart, Michael C. Ramadan, Bashar Solo, Luke Parker, William F. Beaulieu-Jones, Brett K. |
| contents | We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data in the entire context of a patient's hospitalization and so can flag anomalous events that rule-based approaches would consider within a normal range. We demonstrate that the events our model flags are significant for predicting downstream patient outcomes and that a fraction of events identified as carrying little information can safely be dropped. Additionally, we show how informativeness can help interpret the predictions of prognostic models trained on foundation model-derived representations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_22798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models Burkhart, Michael C. Ramadan, Bashar Solo, Luke Parker, William F. Beaulieu-Jones, Brett K. Machine Learning We present a foundation model-derived method to identify highly informative tokens and events in electronic health records. Our approach considers incoming data in the entire context of a patient's hospitalization and so can flag anomalous events that rule-based approaches would consider within a normal range. We demonstrate that the events our model flags are significant for predicting downstream patient outcomes and that a fraction of events identified as carrying little information can safely be dropped. Additionally, we show how informativeness can help interpret the predictions of prognostic models trained on foundation model-derived representations. |
| title | Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.22798 |