arXiv 2404.008262024-04-04cs.CL

使用大语言模型从儿科患者病历中提取健康的社会决定因素：新型语料库与方法

Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods

University of Washington（华盛顿大学）；George Mason University（乔治梅森大学）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Boston Children's Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）

Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J Dobbins, Namu Park, Michael Leu, Abby R. Rosenberg, Kevin Lybarger, Fei Xia, Ozlem Uzuner, Meliha Yetisgen

备注：: 12 pages, 2 figures and 3 tables. Accepted by LREC-COLING 2024

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AI总结：

本文构建儿科SDoH标注语料库PedSHAC，并评估基于LLM微调和GPT-4上下文学习的自动提取方法，在事件论元与触发词上分别取得78.4和82.3 F1。

摘要：

健康的社会决定因素（SDoH）在塑造健康结局方面发挥着关键作用，尤其是在儿科人群中，干预措施可能产生长期影响。SDoH常在电子健康记录（EHR）中得到研究，EHR为多样化的患者数据提供了丰富的存储库。在本工作中，我们提出了一个新型标注语料库——儿科社会史标注语料库（Pediatric Social History Annotation Corpus，PedSHAC），并使用大语言模型（LLM）的微调和上下文学习方法评估详细SDoH表示的自动提取。PedSHAC包含来自华盛顿大学（University of Washington，UW）医院系统内儿科患者的1,260份临床病历中的社会史章节标注。PedSHAC采用基于事件的标注方案，捕获十个不同的健康决定因素，涵盖居住与经济稳定性、既往创伤、教育获取、物质使用史以及心理健康，总体标注者一致性为81.9 F1。我们提出的基于LLM微调的提取器在事件论元上实现了78.4 F1的高性能。使用GPT-4的上下文学习方法在仅有少量标注样本的情况下展现出可靠SDoH提取的前景，事件触发词的提取性能达到82.3 F1。

英文摘要：

Social determinants of health (SDoH) play a critical role in shaping health outcomes, particularly in pediatric populations where interventions can have long-term implications. SDoH are frequently studied in the Electronic Health Record (EHR), which provides a rich repository for diverse patient data. In this work, we present a novel annotated corpus, the Pediatric Social History Annotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods with Large Language Models (LLMs). PedSHAC comprises annotated social history sections from 1,260 clinical notes obtained from pediatric patients within the University of Washington (UW) hospital system. Employing an event-based annotation scheme, PedSHAC captures ten distinct health determinants to encompass living and economic stability, prior trauma, education access, substance use history, and mental health with an overall annotator agreement of 81.9 F1. Our proposed fine-tuning LLM-based extractors achieve high performance at 78.4 F1 for event arguments. In-context learning approaches with GPT-4 demonstrate promise for reliable SDoH extraction with limited annotated examples, with extraction performance at 82.3 F1 for event triggers.

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