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.

社会卫生决定因素（SDoH）在塑造健康结果方面起着关键作用，特别是在儿科人群中，干预措施可能具有长期影响。本研究提出了一个新的标注语料库，儿科社会历史标注语料库（PedSHAC），并使用经过精细调优和上下文学习方法结合大型语言模型（LLMs）来评估详细的 SDoH 表征的自动提取。PedSHAC 包括从华盛顿大学医院系统的儿科患者的 1,260 个临床记录中获取的已标注的社会历史部分。采用一个基于事件的标注方案，PedSHAC 涵盖了十个不同的健康决定因素，包括生活和经济稳定性、先前的创伤、教育机会、物质使用历史和心理健康，整体标注一致性为 81.9 F1。我们提出的精细调整的基于 LLM 的提取器以 78.4 F1 的事件参数高性能实现。搭配 GPT-4 的上下文学习方法在有限的标注示例情况下展现了可靠的 SDoH 提取的前景，事件触发器的提取性能达到 82.3 F1。