Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.
Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.
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DOI：
10.34133/2021/9759016
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发表时间：
2021-01-01
期刊：
影响因子：
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通讯作者：
Zhang, Rui
中科院分区：
文献类型：
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作者：
Bompelli, Anusha;Wang, Yanshan;Zhang, Rui
背景越来越多的证据表明，健康的社会和行为决定因素（SBDH）在广泛的健康结果中发挥着重要作用。电子健康记录（EHR）已被广泛用于人工智能（AI）时代的观察性研究。然而，已经有有限的审查如何使大部分SBDH信息从EHR使用AI approaches. Methods。在六个数据库中进行了系统检索，以查找最近发表的相关同行评审出版物。通过筛选和评价文章确定相关性。基于选定的相关研究，提供了利用EHR数据中SBDH信息的AI算法的方法学分析。我们的合成是由SBDH类别的分析，SBDH和医疗保健相关状态之间的关系，从临床笔记中提取SBDH的自然语言处理（NLP）方法以及使用SBDH进行健康结果的预测模型驱动的。SBDH和健康结果之间的关联是复杂多样的;可能涉及几种途径。使用NLP技术来支持SBDH和其他临床想法的提取，简化了从临床数据中识别和提取基本概念的过程，有效地解锁了非结构化数据，并有助于解决非结构化数据相关问题。尽管已知SBDH和疾病之间的关联，SBDH因素很少被调查作为干预措施，以改善患者的结果。了解SBDH以及如何使用NLP方法和预测模型从EHR收集SBDH数据，可以提高影响患者健康的卫生政策变化的机会，最终促进健康和健康公平。
Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.