Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients

TL;DR

This paper develops and validates interpretable machine learning models to predict COVID-19 severity and mortality in Indian patients, aiding resource allocation and patient triage during the pandemic.

Contribution

It introduces two novel predictive models based on routine blood parameters, achieving high accuracy and integrating them into a web app for practical deployment.

Findings

Models achieved 86.3% and 88.06% accuracy.

AUC-ROC scores were 0.91 and 0.92.

Models are interpretable and deployed via a web app.

Abstract

As the second wave in India mitigates, COVID-19 has now infected about 29 million patients countrywide, leading to more than 350 thousand people dead. As the infections surged, the strain on the medical infrastructure in the country became apparent. While the country vaccinates its population, opening up the economy may lead to an increase in infection rates. In this scenario, it is essential to effectively utilize the limited hospital resources by an informed patient triaging system based on clinical parameters. Here, we present two interpretable machine learning models predicting the clinical outcomes, severity, and mortality, of the patients based on routine non-invasive surveillance of blood parameters from one of the largest cohorts of Indian patients at the day of admission. Patient severity and mortality prediction models achieved 86.3% and 88.06% accuracy, respectively, with an AUC-ROC of 0.91 and 0.92. We have integrated both the models in a user-friendly web app calculator, https://triage-COVID-19.herokuapp.com/, to showcase the potential deployment of such efforts at scale.