Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

ABSTRACT:

Objectives

To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19.

Methods

We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients.

Results

There was no significant difference in the prevalence

SUBMITTER: Feng Z 

PROVIDER: S-EPMC8046645 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature