ObjectiveThe COVID-19 pandemic generated a massive amount of clinical data, which potentially holds yet undiscovered answers related to COVID-19
morbidity,
mortality,
long term effects, and
therapeutic solutions. The objective of this study was to generate insights on COVID-19
mortality-associated factors and identify potential new
therapeutic options for COVID-19
patients by employing
artificial intelligence analytics on real-world data. MethodsA Bayesian statistics-based
artificial intelligence data analytics tool (bAIcis(R)) within Interrogative
Biology(R) platform was used for network
learning, inference
causality and hypothesis generation to analyze 16,277
PCR positive
patients from a database of 279,281
inpatients and
outpatients tested for SARS-CoV-2 infection by
antigen, antibody, or
PCR methods during the first
pandemic year in Central
Florida. This approach generated causal networks that enabled unbiased identification of significant predictors of
mortality for specific COVID-19
patient populations. These findings were validated by
logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping. ResultsWe found that in the
SARS-CoV-2 PCR positive
patient cohort, early use of the
antiemetic agent
ondansetron was associated with increased
survival in mechanically ventilated
patients. ConclusionsThe results demonstrate how real world COVID-19 focused
data analysis using
artificial intelligence can generate valid insights that could possibly support
clinical decision-making and minimize the
future loss of lives and
resources.