  Many countries are now experiencing the third wave of the COVID-19 pandemic
straining the healthcare resources with an acute shortage of hospital beds and
ventilators for the critically ill patients. This situation is especially worse
in India with the second largest load of COVID-19 cases and a relatively
resource-scarce medical infrastructure. Therefore, it becomes essential to
triage the patients based on the severity of their disease and devote resources
towards critically ill patients. Yan et al. 1 have published a very pertinent
research that uses Machine learning (ML) methods to predict the outcome of
COVID-19 patients based on their clinical parameters at the day of admission.
They used the XGBoost algorithm, a type of ensemble model, to build the
mortality prediction model. The final classifier is built through the
sequential addition of multiple weak classifiers. The clinically operable
decision rule was obtained from a 'single-tree XGBoost' and used lactic
dehydrogenase (LDH), lymphocyte and high-sensitivity C-reactive protein
(hs-CRP) values. This decision tree achieved a 100% survival prediction and 81%
mortality prediction. However, these models have several technical challenges
and do not provide an out of the box solution that can be deployed for other
populations as has been reported in the "Matters Arising" section of Yan et al.
Here, we show the limitations of this model by deploying it on one of the
largest datasets of COVID-19 patients containing detailed clinical parameters
collected from India.
