从电子健康记录中提取健康的社会决定因素,使用自然语言处理
Zhenghua Chen1,2, Patricia Lasserre2, Angela Lin1,3
1BC Cancer Kelowna, Kelowna, Canada.
JCO clinical cancer informatics
|July 23, 2025
概括
这项研究开发了一种人工智能驱动的自然语言处理管道,从电子健康记录中提取健康的社会决定因素 (SDoH),实现人口健康研究的高准确性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 人口健康管理 人口健康管理
背景情况:
- 健康的社会决定因素 (SDoH) 显著影响健康结果和不平等.
- 从电子健康记录 (EHR) 中提取SDoH对于政策制定和研究至关重要.
- 自动提取方法,如AI和NLP,提供更高的效率和成本效益.
研究的目的:
- 通过基于自然语言处理 (NLP) 的AI管道,自主地从EHR中提取全面的SDoH细节.
- 评估开源NLP管道的性能与SDoH提取的工业基准相比.
- 为了确定在瘤病患者群体中普遍存在的关键SDoH因素.
主要方法:
- 一个精心策划的数据集1000 BC癌症临床文档被用于培训和验证.
- 开发和优化了一个开源的NLP管道,考虑了字体位置的子类型.
- 优化管道被应用于从13,258份瘤学文件中提取SDoH,与工业基准进行比较.
主要成果:
- 开源的NLP管道实现了0.88的F1平均得分,比基准高出5%的成绩.
- 管道成功提取了13个SDoH因子和超过6万个SDoH实例.
- 经常提取的SDoH因素包括烟草使用,就业,婚姻状况,酒精消费和生活状况.
结论:
- 一个NLP管道证明了从临床笔记中提取SDoH因素的巨大潜力.
- 开发的管道表现出强的表现,特别是在有限的数据.
- 对于NLP管道的更广泛的机构应用,需要对数据集进行特定的调整.
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