用病历文本+大模型，提前识别癫痫，降低误诊风险。
EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models
- 用大语言模型分析电子病历中的临床笔记，自动筛查癫痫。
- 在MIMIC-IV数据集上AUC达0.875，在密歇根大学私有数据集上达0.980。
- 医生配合该模型诊断准确率提升10.9%，适合资源有限地区使用。
癫痫与心因性非癫痫发作常表现为相似的抽搐症状，但治疗方式截然不同。误诊普遍，易导致诊断延迟、不必要的治疗及患者健康受损。尽管长时视频脑电图是诊断金标准，但其高成本和可及性差限制了及时诊断。本文提出一种低成本、高效的早期癫痫检测方法EpiScreen，利用电子健康记录中常规收集的临床笔记进行分析。通过在标注笔记上微调大语言模型，EpiScreen在MIMIC-IV数据集上达到最高0.875的AUC，而在明尼苏达大学私有队列中达到0.980。在医生与AI协作场景下，经EpiScreen辅助的神经科医生诊断表现比独立专家提升最多10.9%。整体表明，EpiScreen能有效支持早期癫痫筛查，实现及时、经济的诊断，减少延误并避免不必要干预，尤其适用于医疗资源匮乏地区。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。