Severity and Mortality Prediction Models to Triage Indian COVID-19 Patients
This work addresses the need for effective patient triaging to manage limited hospital resources during the COVID-19 pandemic in India, representing an incremental application of machine learning to a specific domain.
The authors tackled the problem of predicting COVID-19 severity and mortality in Indian patients using routine blood parameters, achieving 86.3% accuracy for severity and 88.06% accuracy for mortality with AUC-ROC scores of 0.91 and 0.92.
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.