{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T13:46:37Z","timestamp":1773323197319,"version":"3.50.1"},"reference-count":18,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T00:00:00Z","timestamp":1605484800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["bpspubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pharmacology Res &amp; Perspec"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Opioid use disorder (OUD) affects an estimated 16 million people worldwide. The diagnosis of OUD is commonly delayed or missed altogether. We aimed to test the utility of machine learning in creating a prediction model and algorithm for early diagnosis of OUD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects and methods<\/jats:title><jats:p>We analyzed data gathered in a commercial claim database from January 1, 2006, to December 31, 2018 of 10 million medical insurance claims from 550\u00a0000 patient records. We compiled 436 predictor candidates, divided to six feature groups \u2010 demographics, chronic conditions, diagnosis and procedures features, medication features, medical costs, and episode counts. We employed the Word2Vec algorithm and the Gradient Boosting trees algorithm for the analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The c\u2010statistic for the model was 0.959, with a sensitivity of 0.85 and specificity of 0.882. Positive Predictive Value (PPV) was 0.362 and Negative Predictive Value (NPV) was 0.998. Significant differences between positive OUD\u2010 and negative OUD\u2010 controls were in the mean annual amount of opioid use days, number of overlaps in opioid prescriptions per year, mean annual opioid prescriptions, and annual benzodiazepine and muscle relaxant prescriptions. Notable differences were the count of intervertebral disc disorder\u2010related complaints per year, post laminectomy syndrome diagnosed per year, and pain disorders diagnosis per year. Significant differences were also found in the episodes and costs categories.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The new algorithm offers a mean 14.4\u00a0months reduction in time to diagnosis of OUD, at potential saving in further morbidity, medical cost, addictions and mortality.<\/jats:p><\/jats:sec>","DOI":"10.1002\/prp2.669","type":"journal-article","created":{"date-parts":[[2020,11,17]],"date-time":"2020-11-17T06:52:20Z","timestamp":1605595940000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Development of a machine learning algorithm for early detection of opioid use disorder"],"prefix":"10.1002","volume":"8","author":[{"given":"Zvi","family":"Segal","sequence":"first","affiliation":[{"name":"Diagnostic Robotics Inc. Ariel University Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kira","family":"Radinsky","sequence":"additional","affiliation":[{"name":"Diagnostic Robotics Inc. Ariel University Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guy","family":"Elad","sequence":"additional","affiliation":[{"name":"Diagnostic Robotics Inc. Ariel University Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gal","family":"Marom","sequence":"additional","affiliation":[{"name":"Diagnostic Robotics Inc. Ariel University Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moran","family":"Beladev","sequence":"additional","affiliation":[{"name":"Diagnostic Robotics Inc. Ariel University Aviv Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maor","family":"Lewis","sequence":"additional","affiliation":[{"name":"Diagnostic Robotics Inc. 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