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 is a research paper published in JMIR Public Health and Surveillance (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.
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NIDA NIH HHS
Grant: R01 DA051464
NIDA NIH HHS
Grant: R25 DA035692
NIDA NIH HHS
Grant: UG1 DA049467
AHRQ HHS
Grant: K12 HS026385
National Institutes of Health
Grant: 3K12HS026385-03S1
PA-20-072: Supplement to A Chicago Center of Excellence in Learning Health Systems Research Training (ACCELERAT)
National Institutes of Health
Grant: 1UG1DA049467-01
Great Lakes Node of the Drug Abuse Clinical Trials Network
National Institutes of Health
Grant: 5R25DA035692-08
The UCLA HIV/AIDS, Substance Abuse, and Trauma Training Program
National Institutes of Health
Grant: 3R01DA051464-02S1
Building a Substance Use Data Commons for Public Health Informatics
FWCI
0.40
Citation Percentile
0.6%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
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