优化大型语言模型用于检测慢性疾病中抑郁/焦虑症状的患者通信
Jiyeong Kim1, Stephen P Ma2,3,4, Michael L Chen5
1Stanford Center for Digital Health, Department of Medicine, Stanford University, Stanford, CA, USA. jykim3@stanford.edu.
NPJ digital medicine
|October 1, 2025
概括
大型语言模型 (LLM) 显示出从安全消息中检测糖尿病患者的抑郁和焦虑的希望. 拉玛3.1 405B获得了高准确度,这表明LLM可以帮助慢性疾病患者进行心理健康查.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 心理健康查 心理健康查
背景情况:
- 患有糖尿病的患者面临着伴随性抑郁症和焦虑症的更高风险.
- 糖尿病的有效管理因这些精神健康并发症而复杂化.
- 及时检测心理健康症状对于综合患者护理至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 在从安全的患者信息中识别抑郁和焦虑症状方面的性能.
- 确定在临床环境中检测这些心理健康状况的最佳LLM方法.
- 评估LLM在改善慢性疾病患者心理健康查方面的潜力.
主要方法:
- 应用多种LLM方法:工程提示,系统人格,温度调整和零射击/少数射击学习.
- 测试了五种不同的LLM,以检测它们检测抑郁和焦虑症状的能力.
- 使用F-1分数和绩效评估准确度等指标,包括患者健康问卷-4 (PHQ-4).
主要成果:
- 在5个LLM中,有3个表现出色,F-1分数和准确度均超过90%.
- 拉玛 3.1 405B实现了最高的性能,F-1 93%和准确度使用零射击学习方法.
- 法律学士表现出对二进制分类和处理复杂指标的熟练程度,尽管在具有挑战性的案件中注意到了一些不一致性.
结论:
- LLM显示出有很大的潜力,有助于及时查和转诊的糖尿病患者经历抑郁或焦虑.
- 这些发现提供了实证数据,支持将LLM整合到慢性疾病患者护理的现实世界分拣系统中.
- 进一步的现实生活评估是有必要的,以解决不一致性,并充分利用在慢性疾病的心理健康护理LLM的能力.
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