{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T07:07:14Z","timestamp":1788505634674,"version":"build-2803163510"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["T35DK104689"],"award-info":[{"award-number":["T35DK104689"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015615","name":"National Drug Abuse Treatment Clinical Trials Network","doi-asserted-by":"publisher","award":["3UG1DA015831-18S7"],"award-info":[{"award-number":["3UG1DA015831-18S7"]}],"id":[{"id":"10.13039\/100015615","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Richard K. Gershon, M.D., Endowed Medical Student Research Fellowship"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objective<\/jats:title>\n<jats:p>We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>This was a retrospective study of ED visits from 2013\u20132020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients \u226518 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>In total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2\u201396.8%, rates of one-year ED returns from 9\u201334%, rates of one-year opioid event 10\u201317%, rates of medications for opioid use disorder from 17\u201343%, and a median Carlson comorbidity index of 2\u20138. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>Our results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0291572","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T17:24:32Z","timestamp":1694798672000},"page":"e0291572","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["Computational phenotypes for patients with opioid-related disorders presenting to the emergency department"],"prefix":"10.1371","volume":"18","author":[{"given":"R. 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