Predicting special care during the COVID-19 pandemic: a machine learning approach

Bezzan, Vitor P.; Rocco, Cleber D.

More than ever, COVID-19 is putting pressure on health systems worldwide, especially in Brazil. In this study, we propose a method based on statistics and machine learning that uses blood lab exam data from patients to predict whether patients will require special care (hospitalization in regular or special-care units). We also predict the number of days the patients will stay under such care. The two-step procedure developed uses Bayesian Optimisation to select the best model among several candidates. This leads us to final models that achieve 0.94 area under ROC curve performance for the first target and 1.87 root mean squared error for the second target (which is a 77% improvement over the mean baseline)—making our model ready to be deployed as a decision system that could be available for everyone interested. The analytical approach can be used in other diseases and can help to plan hospital resources in other contexts.