Early epilepsy detection from electronic health records with large language models

Epilepsy and psychogenic non-epileptic seizures often present with similar seizure-like manifestations but require fundamentally different management strategies. Misdiagnosis is common and can lead to prolonged diagnostic delays, unnecessary treatments, and substantial patient morbidity. Although prolonged video-electroencephalography is the diagnostic gold standard, its high cost and limited accessibility hinder timely diagnosis.

Here, we developed a low-cost, effective approach, EpiScreen, for early epilepsy detection by utilizing routinely collected clinical notes from electronic health records. Through fine-tuning large language models on labeled notes, EpiScreen achieved an AUC of up to 0.875 on the MIMIC-IV dataset and 0.980 on a private cohort of the University of Minnesota. In a clinician-AI collaboration setting, EpiScreen-assisted neurologists outperformed unaided experts by up to 14.3%.

Overall, this study demonstrates that EpiScreen supports early epilepsy detection, facilitating timely and cost-effective screening that may reduce diagnostic delays and avoid unnecessary interventions, particularly in resource-limited regions. The Center for Learning Health System Sciences at the University of Minnesota supported this study. This work received financial support from the National Institutes of Health, including the National Center for Complementary and Integrative Health (R01AT009457), the National Institute on Aging (R01AG078154), and the National Cancer Institute (R01CA287413).

The content of this article represents the authors’ perspectives and does not necessarily reflect the official views of the NIH. Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA Shuang Zhou, Kai Yu, Huixue Zhou, Min Zeng, Feng Xie & Rui Zhang College of Science and Engineering, University of Minnesota, Minneapolis, MN, USA Department of Neurology, University of Minnesota, Minneapolis, MN, USA Rui Zhang is Associate Editor of npj Digital Medicine. Rui Zhang was not involved in the journal’s review of, or decisions related to, this manuscript.

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