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利用开源大型语言模型来识别患有罕见遗传性动脉动脉病的未被诊断的患者

Pankhuri Singhal¹, Zilinghan Li², Ze Yang³

¹Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.

medRxiv : the preprint server for health sciences

|September 15, 2025

概括

使用大型语言模型 (LLM) 的新管道有助于识别罕见的遗传动脉动病患者进行遗传检测. 该工具分析临床记录以标记潜在病例,改善早期诊断和患者的治疗结果.

科学领域:

遗传学是一个遗传学.
医疗信息学医疗信息学
心脏病学心脏病学

背景情况:

由于各种症状,罕见的遗传性动脉动脉病常常未被诊断出来,导致严重的心脏事件.
早期遗传检测对于主动干预至关重要,但依赖于医生的认可和转诊.
需要自动查来识别不符合典型诊断模式的患者.

研究的目的:

开发和验证一个开源的大型语言模型 (LLM) 支持的管道,用于推在罕见的遗传动脉病中进行基因测试.
利用提取增强生成 (RAG) 来处理临床笔记和识别有风险的患者.

主要方法:

开发了一个LLM管道,集成RAG在遗传性关节病体上.
在宾夕法尼亚大学医学生物库中使用22510名患者进度笔记验证了管道,这些笔记来自500名患者 (250例,250例对照).
使用准确度,精度,灵敏度和F1分数等指标评估管道性能.

主要成果:

该管道成功分类了425名499名患者中的425名.
实现了0.852的患者级推准确度,0.889的精度和0.803的灵敏度.
表现出强的表现,F1得分为0.844和F3得分为0.811.

结论:

与RAG一起启用LLM的工作流显示出识别需要为罕见遗传动脉动病症进行遗传检测的患者的巨大潜力.
自动化对自由文本临床笔记的分析可以改善早期疾病检测和患者的治疗结果.
这种方法可以支持未被诊断的患者识别,并促进及时干预.

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