Early Prediction Model for Critical Illness of Hospitalized COVID-19 Patients Based on Machine Learning Techniques.

ABSTRACT:

Motivation

Patients with novel coronavirus disease 2019 (COVID-19) worsen into critical illness suddenly is a matter of great concern. Early identification and effective triaging of patients with a high risk of developing critical illness COVID-19 upon admission can aid in improving patient care, increasing the cure rate, and mitigating the burden on the medical care system. This study proposed and extended classical least absolute shrinkage and selection operator (LASSO) logistic regression to objectively identify clinical determination and risk factors for the early identification of patients at high risk of progression to critical illness at the time of hospital admission.

Methods

In this retrospective multicenter study, data of 1,929 patients with COVID-19 were assessed.

SUBMITTER: Fu Y 

PROVIDER: S-EPMC9168534 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature