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.

翻译：癫痫与心因性非癫痫发作常呈现相似的发作样表现，但需采取截然不同的管理策略。误诊现象普遍存在，可导致诊断延迟延长、非必要治疗及显著的患者致残率。尽管长程视频脑电图是诊断金标准，但其高成本与有限可及性阻碍了及时诊断。本研究开发了一种低成本、高效的方法EpiScreen，通过利用电子健康记录中常规收集的临床笔记实现早期癫痫检测。通过在有标注的笔记上微调大型语言模型，EpiScreen在MIMIC-IV数据集上实现了最高0.875的AUC，在明尼苏达大学私人队列中达到0.980。在临床医生-AI协作场景中，EpiScreen辅助下的神经科医生表现优于未辅助专家达10.9%。总体而言，本研究表明EpiScreen支持早期癫痫检测，促进及时且经济有效的筛查，可减少诊断延迟并避免非必要干预，尤其在资源受限地区。