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Development and Validation of a Machine Learning… : Epidemiology



From the aCenter for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University, New York, NY, USA

bDivision of Health Policy and Management, School of Public Health, University of California, Berkeley, Berkeley, CA, USA

cDepartment of Epidemiology, School of Public Health, Brown University, Providence, RI, USA

dCenter for Health Data and Analysis, Rhode Island Department of Health, Providence, RI, USA

eDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA

fCenter for Urban Science and Progress, New York University, New York, NY, USA

gDepartment of Computer Science, Courant Institute for Mathematical Sciences, New York University, New York, NY, USA

hRobert F. Wagner Graduate School of Public Service, New York University, New York, NY, USA.

Submitted March 17, 2023; accepted November 20, 2023

The results reported herein correspond to specific aims of grant R01DA046620 to investigators B.D.L.M. and M.C. from the National Institute on Drug Abuse, where R01DA046620 is the project number, B.D.L.M. and M.C. are multiple Principal Investigators, and the National Institute on Drug Abuse is the funding agency. This work also was supported by grant T32DA007233 from the National Institute on Drug Abuse.

The authors report no conflicts of interest.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).

B.A., R.C.S, J.A., and D.B.N. have joint first and senior authorship.

The data used in this study are not available for replication due to data use restrictions established with the Rhode Island Department of Health. Demonstration code is available at the following GitHub repository: https://github.com/pph-collective/provident-model.

Correspondence: Bennett Allen, Department of Population Health, Center for Opioid Epidemiology and Policy, New York University, Grossman School of Medicine, 180 Madison Avenue, 5th Floor, New York, NY 10016. E-mail: [email protected].



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