Machine Learning to Predict Disease Severity and Progression in Hospitalized COVID-19 Patients Using Laboratory Data on Admission

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Background: Herein, we aimed to develop and test machine learning (ML) models to predict disease severity and/or progression in hospitalized COVID-19 patients through baseline laboratory features. Methods: In this retrospective study of hospitalized COVID-19 patients admitted to a tertiary care center, we evaluated routine admission data to determine the accuracy rates of different ML algorithms: k-nearest neighbor classifier, bagging classifier, random forest (RF), and decision tree. These models were compared over three outcomes: those who needed oxygen supplementation vs. who did not on admission (Analysis 1, n: 180), those who later developed oxygen requirement vs. those who did not (Analysis 2, n: 112), and those who needed invasive mechanical ventilation vs. those who did not during hospitalization (Analysis 3, n: 164). Results: The median age of the patients was 55 (44-68) years, with males constituting 47.2% of the subjects. At admission, 37.8% of the patients required oxygen supplementation. During hospitalization, 17.5% needed mechanical ventilation, and 8.3% died. For all analyses, RF had the highest accuracy in classifying the need for oxygen supplementation on admission (89.4%) or during hospitalization (91.1%) and for invasive mechanical ventilation (92.2%). These were followed by a bagging classifier for Analysis 1 (88.3%) and Analysis 3 (91.0%) and by a decision tree for Analysis 2 (88.4%). C-reactive protein, monocyte distribution width, and high-sensitive troponin-T were the most crucial laboratory contributors to Analysis 1, Analysis 2, and Analysis 3, respectively. Conclusion: Our study showed that ML algorithms could predict the need for oxygen supplementation and mechanical ventilation during hospitalization using baseline laboratory data, suggesting a slight superiority of RF, among others.

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