通过自然语言处理揭示健康影响对不良妊娠结果的社会决定因素
Nidhi Soley1,2, MaKhaila Bentil3, Jash Shah3
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA. nsoley1@jhu.edu.
Scientific reports
|August 9, 2025
概括
从临床笔记中使用NLP模型提取健康的社会决定因素 (SDoH) 是可行的. 这些决定因素,包括物质使用和社会支持,显著影响怀孕并发症.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 计算语言学 计算语言学
背景情况:
- 健康的社会决定因素 (SDoH) 显著影响母亲和婴儿的健康结果.
- 从非结构化的电子健康记录 (EHR) 中提取SDoH是临床研究中持续存在的挑战.
- 确定SDoH对于有针对性的干预和改善妊娠护理至关重要.
研究的目的:
- 评估自然语言处理 (NLP) 模型在从临床笔记中提取健康社会决定因素 (SDoH) 的有效性.
- 在SDoH提取中比较基于规则,词嵌入和上下文语言模型的性能.
- 评估NLP模型在不同数据集中用于SDoH提取的概括性.
主要方法:
- 在MIMIC-III数据库上训练和评估NLP模型 (基于规则的,词嵌入,语境语言模型).
- 在MIMIC-IV数据库上进行外部验证的模型,以评估可通用性.
- 专注于提取社会支持,职业和物质使用,并使用后勤回归分析了它们与怀孕并发症的关联.
主要成果:
- 使用决策树分类器的ClinicalBERT在社会支持提取方面获得了最高的F1评分 (0.92).
- 关键字处理在职业提取方面表现强 (F1:0.74).
- 用随机森林分类器嵌入的单词在物质使用提取 (F1:0.83) 中表现最好.
- 后勤回归表明,怀孕并发症和物质使用 (OR 6.47) 和社会支持 (OR 0.07) 之间存在显著联系.
结论:
- NLP模型是从非结构化的临床文本中提取SDoH的有效工具.
- 提取的SDoH,特别是物质使用和社会支持,在临床上是相关的,并且与妊娠并发症有显著的关联.
- 这种方法有助于更深入地了解SDoH在孕产妇健康中的作用,为改善患者护理和结果铺平了道路.
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