  Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high risks
can be recovered or not depends, to a large extent, on how early they will be
treated appropriately before irreversible consequences are caused to the
patients by the virus. In this research, we reported an explainable, intuitive,
and accurate machine learning model based on logistic regression to predict the
fatality rate of COVID-19 patients using only three important blood biomarkers,
including lactic dehydrogenase, lymphocyte (%) and high-sensitivity C-reactive
protein, and their interactions. We found that when the fatality probability
produced by the logistic regression model was over 0.8, the model had the
optimal performance in that it was able to predict patient fatalities more than
11.30 days on average with maximally 34.91 days in advance, an accumulative
f1-score of 93.76% and and an accumulative accuracy score of 93.92%. Such a
model can be used to identify COVID-19 patients with high risks with three
blood biomarkers and help the medical systems around the world plan critical
medical resources amid this pandemic.
