从临床笔记中提取健康信息的社会决定因素的大型语言模型 - - 跨机构通用化的方法
Vipina K Keloth1, Salih Selek2, Qingyu Chen1
1Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
medRxiv : the preprint server for health sciences
|June 3, 2024
概括
从临床记录中提取健康的社会决定因素 (SDoH) 对于改善健康公平至关重要. 大型语言模型 (LLM) 在多种医疗保健系统中准确识别SDoH因素方面表现有前途.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 公共卫生 公共卫生
背景情况:
- 健康的社会决定因素 (SDoH) 显著影响健康结果和公平.
- 从电子健康记录 (EHR) 中提取详细的SDoH信息是具有挑战性的,因为它存在于非结构化的临床叙述中.
- 现有的自然语言处理 (NLP) 方法用于SDoH提取通常缺乏跨机构和笔记类型的通用性.
研究的目的:
- 开发和评估可通用的NLP模型,以从各种临床笔记中检测健康的社会决定因素 (SDoH) 因素.
- 创建跨机构机构培训和测试SDoH提取模型.
- 将传统机器学习算法的性能与用于SDoH识别的大型语言模型 (LLM) 的性能进行比较.
主要方法:
- 来自四个不同的医疗机构的四个未被识别的临床笔记体的开发.
- 在两个层面上对21个SDoH因子的 corpora进行注释:因子类型和因子值.
- 实施和评估传统的分类算法 (XGBoost,TextCNN,句子BERT) 和一个指令调整的LLM (LLaMA).
主要成果:
- 观察到SDoH文档实践和标签分布在机构,笔记类型和患者队列之间存在显著差异.
- 教学调整的LLM (LLaMA) 实现了卓越的性能,微平均F1分数超过0.9级1和0.84级2注释.
- 虽然模型在单个数据集上表现良好,但跨数据集的概括显示了持续的挑战.
结论:
- 大型语言模型在从临床叙述中提取健康的社会决定因素 (SDoH) 中表现出高效率.
- 跨机构机构和先进的NLP模型对于推进SDoH研究和应用至关重要.
- 需要进一步的研究来克服SDoH提取在各种医疗保健环境中的泛化障碍.
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