Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions-Reference-Cited by-同舟云学术

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions

Published:2019-03-22 Issue:3 Volume:2 Page:e190968
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Lo-Ciganic Wei-Hsuan¹,Huang James L.¹,Zhang Hao H.²,Weiss Jeremy C.³,Wu Yonghui⁴,Kwoh C. Kent⁵,Donohue Julie M.⁶,Cochran Gerald⁷,Gordon Adam J.⁷⁸,Malone Daniel C.⁹,Kuza Courtney C.¹⁰,Gellad Walid F.¹⁰¹¹¹²

Affiliation:

1. Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville

2. Department of Mathematics, University of Arizona, Tucson

3. Carnegie Mellon University, Heinz College, Pittsburgh, Pennsylvania

4. Department of Health Outcomes & Biomedical Informatics, University of Florida, College of Medicine, Gainesville

5. Division of Rheumatology, Department of Medicine, and the University of Arizona Arthritis Center, University of Arizona, Tucson

6. Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania

7. Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City

8. Informatics, Decision-Enhancement, and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah

9. Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson

10. Center for Pharmaceutical Policy and Prescribing, Health Policy Institute, University of Pittsburgh, Pittsburgh, Pennsylvania

11. Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania

12. Center for Health Equity Research Promotion, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

http:///jamanetworkopen/article-pdf/2/3/e190968/17689755/lociganic_2019_oi_190058.pdf

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4. Lock-in programs and the opioid epidemic: a call for evidence.;Roberts;Am J Public Health,2016

5. Predicting aberrant behaviors in opioid-treated patients: preliminary validation of the Opioid Risk Tool.;Webster;Pain Med,2005

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