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人工智能可以取代临床医生评级的抑郁症量表吗? 汉姆莱特的心理测量属性 - 汉密尔顿大语言模型评估工具

Wen Xin¹, Huailiang Yi¹, Jiaqi Song²

¹Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilonguan Clinical Medical School, Beijing 100096, China.

Asian journal of psychiatry

|October 10, 2025

概括

汉密尔顿大型语言模型评估工具 (HAMLET) 与临床医生抑郁症评估有很强的一致性,提供了更容易获得的替代方案. 这种大型语言模型工具证明了可扩展的精神病学评估的可行性.

科学领域:

医疗保健中的人工智能
计算精神病学是一种计算精神病学.
临床评估中的自然语言处理.

背景情况:

汉密尔顿抑郁评分表 (HAMD) 是一种标准的抑郁评估工具,但需要临床医生亲自管理.
大型语言模型 (LLM) 为自动化和可访问的心理健康评估提供了机会.
基于LLM的工具HAMLET是为了复制HAMD-17评估而开发的.

研究的目的:

开发和验证基于LLM的工具 (HAMLET),用于自动化抑郁症评估.
为了比较HAMLET与临床医生评级的HAMD-17和PHQ-9的性能.
评估HAMLET在评估重大抑郁症障碍中的协议和增量有效性.

主要方法:

汉姆莱特是使用Qwen-Max模型开发的,具有特定的温度和top-p设置,以临床医生监督的调整为指导.
60名患有重度抑郁症的患者完成了使用HAMLET,精神科医生评级的HAMD-17和PHQ-9的评估.
使用类内相关系数 (ICC) 和Gwet的AC2量化了协议,并分析了相关性和增量有效性.

主要成果:

汉姆莱特证明了与临床医生HAMD-17评级 (ICC=0.911) 的实质性一致.
哈姆雷特与HAMD分数 (r=0.92) 的相关性比PHQ-9 (r=0.79) 更强,并且提供了增量有效性 (ΔR2=0.252).
项目级协议 (Gwet的AC2) 在大多数项目中超过0.60,在敏感问题上有一些变化.

结论:

汉姆莱特是第一个自主进行HAMD-17评估的LLM,与临床医生评级有显著一致.
这种LLM工具弥合了严格的临床尺度和可访问的自我报告措施之间的差距.
这些发现支持使用LLM进行可扩展和具有成本效益的精神病学评估的可行性,未来的工作将探索多式联络整合.

关键词:

自动化临床评估自动化临床评估汉密尔顿抑郁症评级量表汉密尔顿抑郁症评级量表大型语言模型规模验证规模验证

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