论文标题
自然语言处理（NLP）丰富的社会决定因素和自杀死亡之间的关联
Associations Between Natural Language Processing (NLP) Enriched Social Determinants of Health and Suicide Death among US Veterans
论文作者
论文摘要
重要性：已知健康的社会决定因素（SDOH）与自杀行为的风险增加有关，但是很少有研究从非结构化电子健康记录（EHR）注释中使用SDOH。 目的：研究使用结构化和非结构化数据确定自杀与最近的SDOH之间的关联。 设计：嵌套的病例对照研究。 设置：来自美国退伍军人卫生管理局（VHA）的EHR数据。 参与者：2010年10月1日至2015年9月30日，在美国VHA获得护理的6,122,785名退伍军人。 暴露：SDOH在两年内的最大范围内发生，而没有出现SDOH。 主要结果和措施：自杀死亡的病例与出生年度，队列入学日期，性别和随访期间的4个对照相匹配。我们开发了一个NLP系统来从非结构化的注释中提取SDOH。在非结构化数据上的结构化数据，NLP分别产生六个，八和九SDOH。使用条件逻辑回归估算了调整后的优势比（AOR）和95％置信区间（CI）。 结果：在我们的队列中，有8,821名退伍军人在23,725,382人的随访期间自杀（发病率37.18/100,000人年）。我们的队列主要是男性（92.23％）和白色（76.99％）。在五个常见的SDOH中，作为协变量，NLP提取的SDOH平均占所有SDOH发生的80.03％。通过结构化数据和NLP测量的所有SDOH都与自杀风险增加显着相关。效果最大的SDOH是法律问题（AOR = 2.66，95％CI = .46-2.89），其次是暴力（AOR = 2.12，95％CI = 1.98-2.27）。 NLP提取和结构化的SDOH也与自杀有关。 结论和相关性：NLP提取的SDOH总是与退伍军人自杀风险增加显着相关，这表明NLP在公共卫生研究中的潜力。
Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded six, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18/100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the five common SDOH as covariates, NLP-extracted SDOH, on average, covered 80.03% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.66, 95% CI=.46-2.89), followed by violence (aOR=2.12, 95% CI=1.98-2.27). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.
