Advancements in extracting social determinants of health information from narrative text.
Advancements in extracting social determinants of health information from narrative text.
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从叙述文本中提取健康信息的社会决定因素的进展。
DOI：
10.1093/jamia/ocad121
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Uzuner,Özlem
中科院分区：
文献类型：
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作者：
Lybarger,Kevin;BearDon'tWalk,OliverJ;Yetisgen,Meliha;Uzuner,Özlem
健康的社会决定因素（SDoH）是人们出生、生活、工作和年龄的条件，这些条件影响个人福祉、健康结果和预期寿命。1 SDoH包括一系列非医疗因素，包括物质使用，家庭生活质量，婚姻状况，就业状况，教育，种族，地理和其他影响健康的因素。了解患者SDoH可以为患者的医疗保健提供信息，并有可能改善健康结果和减少健康差距。2，3患者SDoH信息通过结构化数据和自由文本（自然语言）文档（包括患者笔记）记录在电子健康记录（EHR）和其他健康相关数据库中。对于许多SDoH，自由文本描述捕获的社会和行为因素比结构化数据更普遍，更详细。在大规模研究、临床决策支持系统和其他二次使用应用中利用自由文本SDoH信息，需要使用自然语言处理（NLP）自动提取SDoH的关键方面。基于NLP的信息提取将SDoH的非结构化自由文本描述映射到结构化语义表示，该结构化语义表示可以与可用的结构化数据组合以创建更完整的患者简档。
Social determinants of health (SDoH) are the conditions in which people are born, live, work, and age that affect personal well-being, health outcomes, and life expectancy. 1 SDoH include a range of nonmedical factors, including substance use, quality of domestic life, marital status, employment status, education, race, geography, and other factors that impact health. Understanding patient SDoH can inform patient health care and has the potential to improve health outcomes and reduce health disparities. 2, 3 Patient SDoH information is documented in the electronic health record (EHR) and other health-related databases through structured data and free-text (natural language) documents, including patient notes. For many SDoH, the free-text descriptions capture social and behavioral factors with higher prevalence and more detail than is available through structured data. Utilizing free-text SDoH information in large-scale studies, clinical decision-support systems, and other secondary use applications, requires the automatic extraction of key aspects of the SDoH using natural language processing (NLP). NLP-based information extraction maps the unstructured, free-text descriptions of SDoH to structured semantic representations that can be combined with available structured data to create more complete patient profiles.