Learning accurate personalized survival models for predicting hospital discharge and mortality of COVID-19 patients.

ABSTRACT: Since it emerged in December of 2019, COVID-19 has placed a huge burden on medical care in countries throughout the world, as it led to a huge number of hospitalizations and mortalities. Many medical centers were overloaded, as their intensive care units and auxiliary protection resources proved insufficient, which made the effective allocation of medical resources an urgent matter. This study describes learned survival prediction models that could help medical professionals make effective decisions regarding patient triage and resource allocation. We created multiple data subsets from a publicly available COVID-19 epidemiological dataset to evaluate the effectiveness of various combinations of covariates-age, sex, geographic location, and chronic disease status-in learning survival models

SUBMITTER: Kumar N 

PROVIDER: S-EPMC8927593 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature