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In a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. In this study, we used automated machine learning (autoML) to develop and compare between multiple machine learning (ML) models that predict the chance of patient survival from COVID-19 infection and identified the best-performing model. In addition, we investigated which biomarkers are the most influential in generating an accurate model. We believe an ML model such as this could be a useful tool for clinicians stratifying hospitalized SARS-CoV-2 patients.

The data was retrospectively collected from Clinical Looking Glass (CLG) on all patients testing positive for COVID-19 through a nasopharyngeal specimen by real-time RT-PCR and admitted between 3/1/2020-7/3/2020 (4376 patients) at our institution. We collected 47 biomarkers from each patient within 36 hours before or after the index time: RT-PCR positivity, and tracked whether a patient survived or not for one month following this time. We utilized the autoML from H2O.ai, an open source package for R language. The autoML generated 20 ML models and ranked them by area under the precision-recall curve (AUCPR) on the test set. We selected the best model (model_var_47) and chose a threshold probability that maximized F2 score to make a binary classifier: dead or alive. Subsequently, we ranked the relative importance of variables that generated model_var_47 and chose the 10 most influential variables. Next, we reran the autoML with these 10 variables and likewise selected the model with the best AUCPR on the test set (model_var_10 …