Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data
Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data
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DOI：
10.1055/s-0040-1702214
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发表时间：
2020-01-01
影响因子：
2.9
通讯作者：
Elhadad, Noemie
中科院分区：
文献类型：
--
作者：
Feller, Daniel J.;Walk, Oliver J. Bear Don't;Elhadad, Noemie
健康的社会和行为决定因素（SBDH）是环境和行为因素，往往阻碍疾病的管理和导致性传播感染。尽管SBDH很重要，但它们在电子健康记录（EHR）中的记录并不一致，并且通常仅以非结构化格式收集。有证据表明，结构化的数据元素存在于电子病历可以进一步确定SBDH在patient record.Objective探讨SBDH文档和个人SBDH风险因素在患者记录中的存在的自动推理。比较临床笔记和结构化的EHR数据，如实验室测量和诊断，以支持inference.Methods的相对能力，我们试图推断存在的SBDH文件在病人的记录，以及患者的状态，11 SBDH，包括酗酒，无家可归，和性取向。我们比较了仅考虑临床笔记，仅考虑结构化数据以及笔记和结构化数据时的分类性能。我们在几个SBDH风险factors.Results分类模型推断SBDH文档的存在下进行了错误分析取得了良好的性能（F1得分：92.7-78.7; F1被认为是主要的评价指标）。对于推断患者SBDH风险状态的模型，性能是可变的;结果范围从LGBT（女同性恋、男同性恋、双性恋和变性者）状态的F1 = 82.7到静脉注射药物使用的F1 = 28.5。错误分析表明，词汇的多样性和文件的历史SBDH状态的挑战推断患者SBDH状态。三个分类推断特定主题的SBDH文件和10 11例SBDH状态分类达到最高性能时，使用临床笔记和结构化data.Conclusion我们的研究结果表明，结合临床自由文本说明和结构化数据提供了最好的方法在分类患者SBDH状态。在患病率低、词汇多样性高的SBDH中，推断患者SBDH状态最具挑战性。
Background Social and behavioral determinants of health (SBDH) are environmental and behavioral factors that often impede disease management and result in sexually transmitted infections. Despite their importance, SBDH are inconsistently documented in electronic health records (EHRs) and typically collected only in an unstructured format. Evidence suggests that structured data elements present in EHRs can contribute further to identify SBDH in the patient record.Objective Explore the automated inference of both the presence of SBDH documentation and individual SBDH risk factors in patient records. Compare the relative ability of clinical notes and structured EHR data, such as laboratory measurements and diagnoses, to support inference.Methods We attempt to infer the presence of SBDH documentation in patient records, as well as patient status of 11 SBDH, including alcohol abuse, homelessness, and sexual orientation. We compare classification performance when considering clinical notes only, structured data only, and notes and structured data together. We perform an error analysis across several SBDH risk factors.Results Classification models inferring the presence of SBDH documentation achieved good performance (F1 score: 92.7-78.7; F1 considered as the primary evaluation metric). Performance was variable for models inferring patient SBDH risk status; results ranged from F1 = 82.7 for LGBT (lesbian, gay, bisexual, and transgender) status to F1 = 28.5 for intravenous drug use. Error analysis demonstrated that lexical diversity and documentation of historical SBDH status challenge inference of patient SBDH status. Three of five classifiers inferring topic-specific SBDH documentation and 10 of 11 patient SBDH status classifiers achieved highest performance when trained using both clinical notes and structured data.Conclusion Our findings suggest that combining clinical free-text notes and structured data provide the best approach in classifying patient SBDH status. Inferring patient SBDH status is most challenging among SBDH with low prevalence and high lexical diversity.