This paper is in the following e-collection/theme issue:

Public Health Informatics (51) JMIR Theme Issue: COVID-19 Special Issue (2238) Infectious Diseases (non-STD/STI) (1288) Outbreak and Pandemic Preparedness and Management (1529) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1385) Machine Learning (1121) Artificial Intelligence (822) Opioid and Related Substance Abuse Crisis (84) Substance Abuse (300) Electronic Health Records (838) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (462)

Published on in Vol 8 , No 12 (2022) :December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38158, first published .
Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study

Authors of this article:

Hale M Thompson 1, 2 Author Orcid Image ;   Brihat Sharma 1 Author Orcid Image ;   Dale L Smith 1 Author Orcid Image ;   Sameer Bhalla 3 Author Orcid Image ;   Ihuoma Erondu 1 Author Orcid Image ;   Aniruddha Hazra 4 Author Orcid Image ;   Yousaf Ilyas 1 Author Orcid Image ;   Paul Pachwicewicz 1 Author Orcid Image ;   Neeral K Sheth 5 Author Orcid Image ;   Neeraj Chhabra 6 Author Orcid Image ;   Niranjan S Karnik 1 Author Orcid Image ;   Majid Afshar 7 Author Orcid Image
Article Authors Cited by Tweetations (7) Metrics

There are no citations yet available for this article according to Crossref .