用大模型从病历中挖出影响心衰患者30天再入院的社会因素。
Mining Social Determinants of Health for Heart Failure Patient 30-Day Readmission via Large Language Model
- 用大语言模型从非结构化病历中提取社会健康决定因素
- 发现吸烟、交通不便等与30天再入院风险显著相关
- 为降低再入院率提供可操作的临床干预方向
心力衰竭（HF）影响数百万美国人，导致高再入院率，带来重大医疗挑战。社会健康决定因素（SDOH），如经济状况和住房稳定，对健康结果具有关键影响，但在结构化电子健康记录（EHR）中常被忽视，隐藏在非结构化临床笔记中。本研究利用先进的大语言模型（LLMs）从临床文本中提取SDOH，并通过逻辑回归分析其与心衰再入院的关系。研究识别出若干与再入院风险相关的关键SDOH（如吸烟、交通受限），并提供了降低再入院率和改善患者照护的可行建议。
原文摘要 · Abstract (English)
Heart Failure (HF) affects millions of Americans and leads to high readmission rates, posing significant healthcare challenges. While Social Determinants of Health (SDOH) such as socioeconomic status and housing stability play critical roles in health outcomes, they are often underrepresented in structured EHRs and hidden in unstructured clinical notes. This study leverages advanced large language models (LLMs) to extract SDOHs from clinical text and uses logistic regression to analyze their association with HF readmissions. By identifying key SDOHs (e.g. tobacco usage, limited transportation) linked to readmission risk, this work also offers actionable insights for reducing readmissions and improving patient care.
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