A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC= 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC= 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).

Automated summary

This study develops a machine learning model using electronic medical records to predict mortality and hospitalization risk of COVID-19 patients. The model achieves high accuracy in predicting patient's final status and moderate accuracy in predicting hospitalization risk.