# EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models

arXiv: https://arxiv.org/abs/2603.28698 · HTML: https://mlsanity.com/paper/2603.28698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Builds on (most cited first, 15 of 15)

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- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [A Framework for Human Evaluation of Large Language Models in Healthcare Derived from Literature Review](https://mlsanity.com/paper/2405.02559/) (2024)
- [Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches](https://mlsanity.com/paper/2404.14779/) (2024)
- [Large Language Models for Disease Diagnosis: A Scoping Review](https://mlsanity.com/paper/2409.00097/) (2024)
- [A Comparative Study of Pretrained Language Models for Long Clinical Text](https://mlsanity.com/paper/2301.11847/) (2023)
- [A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records](https://mlsanity.com/paper/2311.11483/) (2023)
- [Probabilistic Medical Predictions of Large Language Models](https://mlsanity.com/paper/2408.11316/) (2024)
- [PHICON: Improving Generalization of Clinical Text De-identification Models via Data Augmentation](https://mlsanity.com/paper/2010.05143/) (2020)
- [Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis](https://mlsanity.com/paper/2505.03467/) (2025)
- [Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation](https://mlsanity.com/paper/2509.04534/) (2025)

## Abstract (arXiv)

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 10.9%. 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.