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Development and validation of a machine learning… : Epidemiology


1 Center for Opioid Epidemiology and Policy, Department of Population Health, Grossman School of Medicine, New York University

2 Division of Health Policy and Management, School of Public Health, University of California, Berkeley

3 Department of Epidemiology, School of Public Health, Brown University

4 Center for Health Data and Analysis, Rhode Island Department of Health

5 Division of Epidemiology, School of Public Health, University of California, Berkeley

6 Center for Urban Science and Progress, New York University

7 Department of Computer Science, Courant Institute for Mathematical Sciences, New York University

8 Robert F. Wagner Graduate School of Public Service, New York University

Financial support: The results reported herein correspond to specific aims of grant R01DA046620 to investigators BDLM and MC from the National Institute on Drug Abuse, where R01DA046620 is the project number, BDLM and MC 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.

*Denotes joint first and senior authorship

Conflicts of interest: The authors declare no conflicts of interest.

Acknowledgments: The authors thank Jiaqi Dong, Nicholas Liu-Sontag, Brandon Pachuca, and Yicong Wang of the Center for Urban Science and Progress at New York University for their support with model development.

Data and code availability: 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.

Corresponding author; Bennett Allen, PhD, MPA Center for Opioid Epidemiology and Policy Department of Population Health New York University Grossman School of Medicine 180 Madison Avenue, 4th Floor New York, NY 10016 Phone: 646-501-3708 Email: [email protected]



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