从临床文本中捕捉个人层面的社会决定因素
Jennifer J Liang1, Diwakar Mahajan1, Ananya S Iyengar1
1IBM Research, Yorktown Heights, NY.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
概括
这项研究引入了一个新的数据集,用于从整个临床笔记中提取健康的社会决定因素 (SDOH),而不仅仅是社会历史部分. 该资源旨在通过使关键的非医疗健康因素更容易获得来改善患者护理.
科学领域:
- 临床信息学 临床信息学
- 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) 显著影响患者的结果,但往往被埋在非结构化的临床笔记中.
- 现有的SDOH提取方法受到狭窄的定义的限制,并专注于特定的注释部分.
研究的目的:
- 从整个临床笔记中开发和发布SDOH提取的综合数据集.
- 在临床文本的各个部分描述SDOH的文档.
- 评估基线提取系统和训练在有限部分的有效性.
主要方法:
- 在500个临床笔记中创建一个新的数据集,包含7个SDOH领域的2,007个注释.
- 在整个笔记中对SDOH的注释,不仅限于社会历史部分.
- 开发和评估用于SDOH提取的代币分类和生成模型.
主要成果:
- 该研究描述了SDOH信息在临床笔记中的存在和位置.
- 基线模型使用令牌分类和生成方法证明了SDOH提取的可行性.
- 分析调查了培训的有效性,仅针对社会历史部分,而不是整个笔记.
结论:
- 创建了一个全新的,广泛定义的SDOH数据集,在整个临床笔记中进行注释.
- 数据集和基线系统为推进SDOH提取研究提供了基础.
- 公开发布数据集将有助于进一步研究通过可访问的SDOH数据来改善患者护理.
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