{"ID":2888855,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.22798","arxiv_id":"2507.22798","title":"Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models","abstract":"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.","short_abstract":"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 demon...","url_abs":"https://arxiv.org/abs/2507.22798","url_pdf":"https://arxiv.org/pdf/2507.22798v1","authors":"[\"Michael C. Burkhart\",\"Bashar Ramadan\",\"Luke Solo\",\"William F. Parker\",\"Brett K. Beaulieu-Jones\"]","published":"2025-07-30T16:01:18Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}