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

TL;DR

This paper presents a machine learning-based analytical approach to predict the need for special care and estimate hospital stay duration for COVID-19 patients, aiding healthcare planning and resource allocation.

Contribution

It introduces a two-step machine learning method with Bayesian Optimization that achieves high accuracy in predicting care needs and hospital stay length, adaptable to other diseases.

Findings

Achieved 0.94 AUC for care requirement prediction

Reduced RMSE to 1.87 for stay duration prediction

Model outperforms baseline by 77% in accuracy

Abstract

More than ever COVID-19 is putting pressure on health systems all around the world, especially in Brazil. In this study we propose an analytical approach based on statistics and machine learning that uses lab exam data coming from patients to predict whether patients are going to require special care (hospitalisation 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 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 the planning hospital capacity.