利用大语言模型改进法国电子健康记录中健康社会决定因素的记录
Improving Social Determinants of Health Documentation in French EHRs Using Large Language Models
AI总结：
本研究利用大语言模型从法国临床笔记中提取13类健康社会决定因素，在多个数据集上验证其有效性，并公开两个数据集，显著提升了非英语EHR中SDoH记录的完整性。
摘要：
健康社会决定因素（SDoH）显著影响健康结局，塑造疾病进展、治疗依从性和健康差异。然而，它们在结构化电子健康记录（EHRs）中的记录往往不完整或缺失。本研究提出一种基于大语言模型（LLMs）的方法，用于从法国临床笔记中提取13类SDoH。我们在法国南特大学医院的临床笔记社会史部分上训练了Flan-T5-Large。我们在两个层面评估了该模型：（i）识别SDoH类别及其关联值，以及（ii）提取带有相关时间和定量信息的详细SDoH。模型性能在四个数据集上进行了评估，其中两个我们作为开放资源公开发布。该模型在识别记录良好的类别（如生活状况、婚姻状况、后代、职业、烟草和酒精使用）方面取得了较强性能（F1分数>0.80）。对于训练数据有限或表达高度可变的类别（如就业状况、住房、身体活动、收入和教育），性能较低。我们的模型识别出95.8%至少具有一项SDoH的患者，而来自结构化EHR数据的ICD-10编码仅识别出2.8%。我们的错误分析表明，性能限制与标注不一致、依赖以英语为中心的分词器以及由于模型仅在社交史部分上训练而导致的泛化能力降低有关。这些结果证明了NLP在非英语EHR系统中提高真实世界SDoH数据完整性方面的有效性。
英文摘要：
Social determinants of health (SDoH) significantly influence health outcomes, shaping disease progression, treatment adherence, and health disparities. However, their documentation in structured electronic health records (EHRs) is often incomplete or missing. This study presents an approach based on large language models (LLMs) for extracting 13 SDoH categories from French clinical notes. We trained Flan-T5-Large on annotated social history sections from clinical notes at Nantes University Hospital, France. We evaluated the model at two levels: (i) identification of SDoH categories and associated values, and (ii) extraction of detailed SDoH with associated temporal and quantitative information. The model performance was assessed across four datasets, including two that we publicly release as open resources. The model achieved strong performance for identifying well-documented categories such as living condition, marital status, descendants, job, tobacco, and alcohol use (F1 score > 0.80). Performance was lower for categories with limited training data or highly variable expressions, such as employment status, housing, physical activity, income, and education. Our model identified 95.8% of patients with at least one SDoH, compared to 2.8% for ICD-10 codes from structured EHR data. Our error analysis showed that performance limitations were linked to annotation inconsistencies, reliance on English-centric tokenizer, and reduced generalizability due to the model being trained on social history sections only. These results demonstrate the effectiveness of NLP in improving the completeness of real-world SDoH data in a non-English EHR system.
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。
