Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

ABSTRACT:

Background

To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.

Methods

A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illn

SUBMITTER: Liu Q 

PROVIDER: S-EPMC7947498 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature