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

Public Health Informatics (93) JMIR Theme Issue: COVID-19 Special Issue (2452) Infectious Diseases (non-STD/STI) (1575) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Machine Learning (1465) Artificial Intelligence (1371) Opioid and Related Substance Misuse (147) Substance Abuse (419) Electronic Health Records (1053) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (573)

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 Thompson1, 2 Author Orcid Image ;   Brihat Sharma1 Author Orcid Image ;   Dale L Smith1 Author Orcid Image ;   Sameer Bhalla3 Author Orcid Image ;   Ihuoma Erondu1 Author Orcid Image ;   Aniruddha Hazra4 Author Orcid Image ;   Yousaf Ilyas1 Author Orcid Image ;   Paul Pachwicewicz1 Author Orcid Image ;   Neeral K Sheth5 Author Orcid Image ;   Neeraj Chhabra6 Author Orcid Image ;   Niranjan S Karnik1 Author Orcid Image ;   Majid Afshar7 Author Orcid Image
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