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Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing

机译：用自然语言处理识别与健康的社会决定因素有关的患者

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Social and behavioral factors influence health but are infrequently recorded in electronic health records (EHRs). Here, we demonstrate that psychosocial vital signs can be extracted from EHR data. We processed structured and unstructured EHR data using expert-driven queries and Natural Language Processing (NLP), validating results through structured annotation. We found that although these vital signs are present in EHRs, with 681 structured entries identified for psychosocial concepts, NLP identified a nearly 90-fold increase in patients.

机译：社会和行为因素会影响健康，但在电子健康记录（EHRS）中不经常录制。在这里，我们证明可以从EHR数据中提取心理社会生命体征。我们使用专家驱动的查询和自然语言处理（NLP）处理结构化和非结构化的EHR数据，通过结构注释验证结果。我们发现，尽管这些生命体征存在于EHR中，但是对于心理社会概念确定了681个结构化条目，但NLP确定了患者的近90倍。

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《MEDINFO》|2019年|xl 1091-2035 p. :|共2页
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David Dorr; Cosmin A. Bejan; Christie Pizzimenti; Sumeet Singh; Matt Storer; Ana Quinones;
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中图分类医学与其他学科的关系;
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Identifying Patients with Significant Problems Related to Social Determinants of Health with Natural Language Processing

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