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基于多中心真实世界临床记录的精神病诊断的大型语言模型:一项比较研究

Maoqian Sun^1,2, Jia Yu³, Zhuhong Long²

¹Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, CN.

JMIR medical informatics

|December 18, 2025

概括

大型语言模型 (LLM) 在帮助使用电子健康记录进行精神病诊断方面表现有前途. GPT-4.0表现出强的表现,尽管准确性因年龄和疾病类型而异.

科学领域:

人工智能在医学中的应用
计算精神病学是一种计算精神病学.
医疗信息学医疗信息学

背景情况:

精神病诊断具有挑战性,依赖于主观判断,特别是在资源有限的环境中.
大型语言模型 (LLM) 显示了诊断支持的潜力,但实际数据验证是有限的.

研究的目的:

为了评估和比较多个LLM的诊断性能精神疾病.
使用多中心,现实世界的电子健康记录.

主要方法:

追溯分析了来自中国六个精神病中心的9923个住院患者的电子健康记录.
对GPT-4.0,GPT-3.5和GLM-4-Plus与医生的诊断进行评估.
使用严格准确度和宽松分类指标进行评估,并进行子组分析.

主要成果:

GPT-4.0实现了最高的严格精度 (71.7%) 和宽松的F1得分 (0.881).
情绪障碍和精神分裂症谱系障碍的表现最强.
精度因年龄组而异 (老年人最高,青少年较低),在各中心表现稳定.

结论:

LLM,特别是GPT-4.0,显示了支持精神病诊断的能力与现实世界的EHR数据.
绩效受年龄组和疾病类别的影响.
在临床实施之前,LLM应该作为辅助工具,需要进一步验证.

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