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arXiv · 2507.22798

Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models

Michael C. Burkhart·Bashar Ramadan·Luke Solo·William F. Parker·Brett K. Beaulieu-Jones
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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.

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Michael C. Burkhart, Bashar Ramadan, Luke Solo, William F. Parker, Brett K. Beaulieu-Jones. 2025-07-30. Quantifying surprise in clinical care: Detecting highly informative events in electronic health records with foundation models. https://arxiv.org/abs/2507.22798

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