{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T03:42:07Z","timestamp":1789962127672,"version":"4.0.1"},"reference-count":44,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T00:00:00Z","timestamp":1568851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Suicide &amp;amp; Life Threat Behav"],"published-print":{"date-parts":[[2020,4]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      The Centers for Disease Control and Prevention (\n                      <jats:styled-content style=\"fixed-case\">CDC<\/jats:styled-content>\n                      ) monitor accidental and intentional deaths to answer questions that are critical for the development of effective prevention and resource allocation.\n                      <jats:styled-content style=\"fixed-case\">CDC<\/jats:styled-content>\n                      's National Violent Death Reporting System (\n                      <jats:styled-content style=\"fixed-case\">NVDRS<\/jats:styled-content>\n                      ) is a major innovation in surveillance linking individual\u2010level data from multiple sources. However, suicide underreporting is common, particularly from drug overdose deaths. This study sought to assess machine learning (\n                      <jats:styled-content style=\"fixed-case\">ML<\/jats:styled-content>\n                      ) techniques in quantifying drug overdose suicide underreporting rates.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Clinical, sociodemographic, toxicological, and proximal stressor data on overdose decedents (\n                      <jats:italic>n<\/jats:italic>\n                      \u00a0=\u00a02,665) were extracted from Utah's\n                      <jats:styled-content style=\"fixed-case\">NVDRS<\/jats:styled-content>\n                      from 2012 to 2015. The existing well\u2010determined cases were used to train and test our\n                      <jats:styled-content style=\"fixed-case\">ML<\/jats:styled-content>\n                      models. We assessed and compared multiple machine learning methods including Logistic Regression, Random Forest Classifier, Support Vector Machines, and Artificial Neural Networks. We applied a majority voting methodology to classify undetermined drug overdose deaths.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Overdose suicide rates were estimated to be underreported by 33% across all years, increasing yearly from 29% in 2012 to 37% in 2015. The overall test accuracies for all models ranged from 92.3% to 94.6%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      This research identifies a cost\u2010effective, replicable, and expandable\n                      <jats:styled-content style=\"fixed-case\">ML<\/jats:styled-content>\n                      \u2010based methodology to estimate the true rates of suicide which may be partially masked during the opioid epidemic.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/sltb.12591","type":"journal-article","created":{"date-parts":[[2019,9,19]],"date-time":"2019-09-19T09:44:21Z","timestamp":1568886261000},"page":"333-344","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Discovering the Unclassified Suicide Cases Among Undetermined Drug Overdose Deaths Using Machine Learning Techniques"],"prefix":"10.1111","volume":"50","author":[{"given":"Daphne","family":"Liu","sequence":"first","affiliation":[{"name":"West High School Salt Lake City UT USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mia","family":"Yu","sequence":"additional","affiliation":[{"name":"The College University of Chicago Chicago IL USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeffrey","family":"Duncan","sequence":"additional","affiliation":[{"name":"Utah Department of Health Salt Lake City UT USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Fondario","sequence":"additional","affiliation":[{"name":"Utah Department of Health Salt Lake City UT USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hadi","family":"Kharrazi","sequence":"additional","affiliation":[{"name":"Center for Population Health IT Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"},{"name":"Division of Health Sciences Informatics Johns Hopkins School of Medicine Baltimore MD USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2479-703X","authenticated-orcid":false,"given":"Paul S.","family":"Nestadt","sequence":"additional","affiliation":[{"name":"Department of Psychiatry and Behavioral Sciences Johns Hopkins School of Medicine Baltimore MD USA"},{"name":"Department of Mental Health Johns Hopkins Bloomberg School of Public Health Baltimore MD USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,9,19]]},"reference":[{"key":"e_1_2_5_2_1","unstructured":"Arias E. 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