Extracting social determinants of health from electronic health records using natural language processing: a systematic review.
Extracting social determinants of health from electronic health records using natural language processing: a systematic review.
复制标题
DOI：
10.1093/jamia/ocab170
复制
发表时间：
2021-11-25
期刊：
影响因子：
--
通讯作者：
Pathak J
中科院分区：
文献类型：
--
作者：
Patra BG;Sharma MM;Vekaria V;Adekkanattu P;Patterson OV;Glicksberg B;Lepow LA;Ryu E;Biernacka JM;Furmanchuk A;George TJ;Hogan W;Wu Y;Yang X;Bian J;Weissman M;Wickramaratne P;Mann JJ;Olfson M;Campion TR;Weiner M;Pathak J
健康的社会决定因素（SDoH）是影响患者健康风险和临床结局的非临床倾向。在临床决策中利用SDoH可以潜在地改善诊断，治疗计划和患者结局。尽管在电子健康记录（EHR）中捕获SDoH的兴趣增加，但是这样的信息通常被锁定在非结构化的临床笔记中。自然语言处理（NLP）是从临床文本中提取SDoH信息并扩展其在患者护理和研究中的实用性的关键技术。本文对最先进的NLP方法和工具进行了系统的综述，这些方法和工具专注于从EHR中的非结构化临床文本中识别和提取SDoH数据。于2021年2月使用3个学术数据库（ACL Anthology、PubMed和Scopus）按照系统性综述和荟萃分析（PRISMA）的首选报告项目指南进行了广泛的文献检索。最初共识别出6402篇出版物，在应用研究纳入标准后，选择了82篇出版物进行最终审查。吸烟状态（n = 27），物质使用（n = 21），无家可归（n = 20）和饮酒（n = 15）是最常研究的SDoH类别。无家可归者（n = 7）和其他研究较少的SDoH（例如，教育，财务问题，社会孤立和支持，家庭问题）主要是使用基于规则的方法确定的。相比之下，机器学习方法在识别吸烟状态（n = 13），物质使用（n = 9）和酒精使用（n = 9）方面很受欢迎。NLP提供了从叙述性临床记录中提取SDoH数据的巨大潜力，这反过来又可以帮助开发筛选工具，风险预测模型和临床决策支持系统。
Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
登录
查看更多内容
影响因子：
2.9
作者：
Feller, Daniel J.;Walk, Oliver J. Bear Don't;Elhadad, Noemie
通讯作者：
Elhadad, Noemie
影响因子：
13.8
作者：
Gordon, Deborah D.;Patel, Ihsaan;Perlis, Roy H.
通讯作者：
Perlis, Roy H.
DOI：
10.34133/2021/9759016
发表时间：
2021-01-01
期刊：
Health data science
影响因子：
--
作者：
Bompelli, Anusha;Wang, Yanshan;Zhang, Rui
通讯作者：
Zhang, Rui
DOI：
10.1093/jamia/ocx059
发表时间：
2018-01-01
影响因子：
6.4
作者：
Bejan, Cosmin A.;Angiolillo, John;Denny, Joshua C.
通讯作者：
Denny, Joshua C.
影响因子：
5.5
作者：
Bellows, Brandon K.;DuVall, Scott L.;LaFleur, Joanne
通讯作者：
LaFleur, Joanne