{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T09:08:43Z","timestamp":1792487323672,"version":"4.5.0"},"reference-count":37,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To investigate associations between veterans\u2019 death by suicide and recent SDOHs, identified using structured and unstructured data.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This nested case-control study included veterans who received care under the US Veterans Health Administration from October 1, 2010, to September 30, 2015. A natural language processing (NLP) system was developed to extract SDOHs from unstructured clinical notes. Structured data yielded 6 SDOHs (ie, social or familial problems, employment or financial problems, housing instability, legal problems, violence, and nonspecific psychosocial needs), NLP on unstructured data yielded 8 SDOHs (social isolation, job or financial insecurity, housing instability, legal problems, barriers to care, violence, transition of care, and food insecurity), and combining them yielded 9 SDOHs. Data were analyzed in May 2022.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n Occurrence of SDOHs over a maximum span of 2 years compared with no occurrence of SDOH.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Cases of suicide death were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. Suicide was ascertained by National Death Index, and patients were followed up for up to 2 years after cohort entry with a study end date of September 30, 2015. Adjusted odds ratios (aORs) and 95% CIs were estimated using conditional logistic regression.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Of 6\u202f122\u202f785 veterans, 8821 committed suicide during 23\u202f725\u202f382 person-years of follow-up (incidence rate 37.18 per 100\u202f000 person-years). These 8821 veterans were matched with 35\u202f284 control participants. The cohort was mostly male (42\u202f540 [96.45%]) and White (34\u202f930 [79.20%]), with 6227 (14.12%) Black veterans. The mean (SD) age was 58.64 (17.41) years. Across the 5 common SDOHs, NLP-extracted SDOH, on average, retained 49.92% of structured SDOHs and covered 80.03% of all SDOH occurrences. SDOHs, obtained by structured data and\/or NLP, were significantly associated with increased risk of suicide. The 3 SDOHs with the largest effect sizes were legal problems (aOR, 2.66; 95% CI, 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23), when obtained by combining structured data and NLP.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this study, NLP-extracted SDOHs, with and without structured SDOHs, were associated with increased risk of suicide among veterans, suggesting the potential utility of NLP in public health studies.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.3079","type":"journal-article","created":{"date-parts":[[2023,3,15]],"date-time":"2023-03-15T11:11:49Z","timestamp":1678878709000},"page":"e233079","source":"Crossref","is-referenced-by-count":64,"title":["Associations Between Natural Language Processing\u2013Enriched Social Determinants of Health and Suicide Death Among US 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Amherst"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weisong","family":"Liu","sequence":"additional","affiliation":[{"name":"Miner School of Computer and Information Sciences, University of Massachusetts Lowell"},{"name":"Center for Biomedical and Health Research in Data Sciences, University of Massachusetts Lowell"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack","family":"Tsai","sequence":"additional","affiliation":[{"name":"National Center on Homelessness Among Veterans, US Department of Veterans Affairs, Tampa, Florida"},{"name":"School of Public Health, University of Texas Health Science Center at Houston"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Yu","sequence":"additional","affiliation":[{"name":"Manning College of Information and Computer Sciences, University of Massachusetts Amherst"},{"name":"Center for Healthcare Organization & Implementation Research, Veterans Affairs Bedford Healthcare System, 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