arXiv 2503.113842025-03-14cs.AIcs.CL

优化大型语言模型以检测慢性病中共病抑郁或焦虑症状：来自患者消息的洞见

Optimizing Large Language Models for Detecting Symptoms of Comorbid Depression or Anxiety in Chronic Diseases: Insights from Patient Messages

Stanford University（斯坦福大学）；Stanford School of Medicine（斯坦福医学院）；Stanford Health Care（斯坦福医疗保健系统）；Google LLC（谷歌公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Veterans Affairs Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗保健系统）

Jiyeong Kim, Stephen P. Ma, Michael L. Chen, Isaac R. Galatzer-Levy, John Torous, Peter J. van Roessel, Christopher Sharp, Michael A. Pfeffer, Carolyn I. Rodrig… 展开作者

Jiyeong Kim, Stephen P. Ma, Michael L. Chen, Isaac R. Galatzer-Levy, John Torous, Peter J. van Roessel, Christopher Sharp, Michael A. Pfeffer, Carolyn I. Rodriguez, Eleni Linos, Jonathan H. Chen

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AI总结：

本研究通过提示工程、角色设定、温度调节及零样本/少样本学习优化LLM，从患者消息中检测糖尿病共病抑郁或焦虑，Llama 3.1 405B零样本F-1与准确率均达93%。

摘要：

糖尿病患者发生共病抑郁或焦虑的风险更高，这会使其疾病管理更加复杂。本研究评估了大型语言模型（LLM）从安全患者消息中检测这些症状的性能。我们采用了多种方法，包括工程化提示、系统性角色设定、温度调节以及零样本和少样本学习，以识别性能最佳的模型并提升表现。五个LLM中有三个表现出优异性能（F-1和准确率均超过90%），其中Llama 3.1 405B使用零样本方法在F-1和准确率上均达到93%。尽管LLM在二分类以及处理Patient Health Questionnaire-4等复杂指标方面展现出前景，但在疑难病例中的不一致性仍需进一步的真实场景评估。研究结果突显了LLM辅助及时筛查和转诊的潜力，为真实世界分诊系统提供了有价值的实证知识，这些系统有望改善慢性病患者的心理健康护理。

英文摘要：

Patients with diabetes are at increased risk of comorbid depression or anxiety, complicating their management. This study evaluated the performance of large language models (LLMs) in detecting these symptoms from secure patient messages. We applied multiple approaches, including engineered prompts, systemic persona, temperature adjustments, and zero-shot and few-shot learning, to identify the best-performing model and enhance performance. Three out of five LLMs demonstrated excellent performance (over 90% of F-1 and accuracy), with Llama 3.1 405B achieving 93% in both F-1 and accuracy using a zero-shot approach. While LLMs showed promise in binary classification and handling complex metrics like Patient Health Questionnaire-4, inconsistencies in challenging cases warrant further real-life assessment. The findings highlight the potential of LLMs to assist in timely screening and referrals, providing valuable empirical knowledge for real-world triage systems that could improve mental health care for patients with chronic diseases.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。