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Predicting opioid dependence from electronic health records with machine learning

Published Online: 2019-01-29

Published Print: 2019-12

Authors

Ellis, Randall J.

Wang, Zichen

Genes, Nicholas

Ma’ayan, Avi http://orcid.org/0000-0002-6904-1017
Funding

Funding for this research was provided by:

National Heart, Lung, and Blood Institute (U54-HL127624)

National Cancer Institute (U24-CA224260)

NIH Office of the Director (OT3-OD025467)
License Information

Text and Data Mining valid from 2019-01-29

Version of Record valid from 2019-01-29
More Information

Article History

Received: 27 October 2018

Accepted: 22 January 2019

First Online: 29 January 2019

Ethics approval and consent to participate

: This study has been granted exemption from human-subject research by the Program for the Protection of Human Subjects (PPHS) at the Institutional Review Boards (IRB), Mount Sinai Health System. The project number is HS#:18–00993.

: The authors declare that they have no competing interests.

: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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