使用大型语言模型识别多种长期疾病患者群体:基于人口的研究
Alexander Smith1, Thomas Beaney2, Carinna Hockham2
1Department of Epidemiology and Biostatistics, Imperial College London, London, UK.
NPJ digital medicine
|July 17, 2025
概括
研究人员使用语言模型来识别具有多种长期疾病 (MLTC) 的不同群体. 这种方法有助于通过揭示患者数据中的模式来定制医疗保健,帮助精准医学.
科学领域:
- 计算语言学计算语言学
- 医疗信息学 医疗信息学
- 人口健康 人口健康
背景情况:
- 在多种长期疾病 (MLTC) 中识别模式对于个性化医疗保健至关重要.
- 现有的方法可能无法完全捕捉同时出现的慢性疾病的复杂性.
研究的目的:
- 开发和应用一种语言模型来识别MLTC患者的性别特定群体.
- 利用电子健康记录 (EHR) 数据来发现不同的疾病模式.
主要方法:
- 开发了一个结合DeBERTa语言模型 (EHR-DeBERTa) 的管道.
- 该模型在580万英国患者的纵向EHR数据上进行了预训练.
- 用K-Means分析生成了特定性别的患者嵌入,并确定了集群.
主要成果:
- 在女性中发现了15个群,在男性中发现了17个群.
- 集群被分为低疾病负担,心理健康,心脏代谢,呼吸道和混合疾病组.
- 心脏代谢和精神健康状况在集群中显示出显著的分离,老年患者在心脏代谢组中普遍存在.
结论:
- 大型语言模型 (LLM) 可以为复杂的疾病模式提供可解释的见解.
- 这种方法支持MLTC患者的精准医学.
- 结合临床结果的未来研究可以提高风险预测.
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