将自然语言处理系统适配到新的医疗保健环境中，以识别健康的社会决定因素。
Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.
机构信息
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States; Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, United States.
Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, United States.
出版信息
J Biomed Inform. 2021 Aug;120:103851. doi: 10.1016/j.jbi.2021.103851. Epub 2021 Jun 24.
Abstract
Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort. We developed a transformation of the SDoH outputs of the tool into the OMOP common data model (CDM) for re-use across many potential use cases, yielding performance measures across 8 SDoH classes of precision 0.83 recall 0.74 and F-measure of 0.78.
摘要
社会决定因素健康（SDoH）越来越成为人口健康、医疗保健结果和医疗服务提供的重要因素。然而，这些因素中有许多在结构化的电子健康记录（EHR）数据中无法可靠地捕获。在这项工作中，我们评估并改编了以前发表的自然语言处理（NLP）工具，以纳入范德比尔特大学医学中心急性心肌梗死队列中部署的其他社会风险因素。我们开发了一种工具的 SDoH 输出的转换，将其转换为 OMOP 通用数据模型（CDM），以便在许多潜在用例中重复使用，在 8 个 SDoH 类别的性能指标中，精度为 0.83，召回率为 0.74，F-度量为 0.78。
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