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Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach.

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Title: Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach.
Authors: Ward PJ; Kentucky Injury Prevention and Research Center, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America.; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America., Rock PJ; Kentucky Injury Prevention and Research Center, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America.; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America., Slavova S; Kentucky Injury Prevention and Research Center, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America.; Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America., Young AM; Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America.; Center on Drug and Alcohol Research, College of Medicine, University of Kentucky, Lexington, Kentucky, United States of America., Bunn TL; Kentucky Injury Prevention and Research Center, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America.; Department of Preventive Medicine and Environmental Health, College of Public Health, University of Kentucky, Lexington, Kentucky, United States of America., Kavuluru R; Department of Computer Science, College of Engineering, University of Kentucky, Lexington, Kentucky, United States of America.; Division of Biomedical Informatics, Department of Internal Medicine, College of Medicine, University of Kentucky, Lexington, Kentucky, United States of America.
Source: PloS one [PLoS One] 2019 Oct 16; Vol. 14 (10), pp. e0223318. Date of Electronic Publication: 2019 Oct 16 (Print Publication: 2019).
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, P.H.S.
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Database: MEDLINE Ultimate
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