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

TL;DR

EpiScreen leverages fine-tuned large language models on electronic health records to enable early, cost-effective epilepsy detection, outperforming clinicians and potentially reducing diagnostic delays.

Contribution

The paper introduces EpiScreen, a novel approach using large language models for early epilepsy detection from clinical notes, demonstrating high accuracy and clinical utility.

Findings

EpiScreen achieved AUC up to 0.875 on MIMIC-IV and 0.980 on private data.

Clinician-AI collaboration improved neurologists' performance by up to 10.9%.

EpiScreen supports timely, low-cost epilepsy screening, especially in resource-limited settings.

Abstract

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.