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从医疗文本中检测自杀倾向,使用保护隐私的大型语言模型

Isabella Catharina Wiest¹, Falk Gerrik Verhees², Dyke Ferber³

¹Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; and Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

The British journal of psychiatry : the journal of mental science

|November 5, 2024

概括

大型语言模型 (LLM) 从精神病记录中有效检测自杀风险. 德国的Llama-2模型实现了高精度,灵敏度和特异性,提高了患者护理和紧急监视.

科学领域:

精神病学是一个精神病学.
人工智能的人工智能
自然语言处理自然语言处理.

背景情况:

人工智能 (AI) 在精神疾病分析中取得了适度的成功.
整合临床评估数据可以改善心理健康的AI模型.
这项研究探讨了使用大型语言模型 (LLM) 来检测精神病治疗中的自杀风险.

研究的目的:

从电子健康记录 (EHR) 中提取自杀倾向状态,使用保护隐私的,本地托管的LLMs.
评估Llama-2模型在临床笔记中识别自杀风险的有效性.
评估不同Llama-2变种在自杀性检测中的表现.

主要方法:

对比了多个开源的Llama-2 LLM变体.
从100份精神病医院的入院记录中提取了自杀倾向状态.
使用精度,灵敏度,特异性和F1得分对专家定义的地面真相进行评估.
测试了各种提示策略以优化信息提取.

主要成果:

德国精心调整的Llama-2模型表现出卓越的性能.
在识别自杀倾向时,获得了87.5%的准确性,83.0%的敏感性和91.8%的特异性.
在不同的提示设计中观察到显著的灵敏度和特异性的改善.

结论:

LLM,特别是Llama-2,可以准确地从精神病学记录中提取自杀倾向信息,同时保持数据隐私.
这项技术有可能用于精神病紧急监控系统.
通过提高质量控制和研究能力,LLM可以改善临床自杀倾向的管理.

关键词:

大型语言模型.电子健康记录是电子健康记录.自然语言处理自然语言处理.精神疾病检测精神疾病检测自杀倾向自杀倾向自杀倾向自杀倾向

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