Machine learning-based mortality prediction models for smoker COVID-19 patients.

ABSTRACT:

Background

The large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of resources and services. In recent years, mortality prediction models have shown a potential in alleviating this issue; however, these models are susceptible to biases in specific subpopulations with different risks of mortality, such as patients with prior history of smoking. The current study aims to develop a machine learning-based mortality prediction model for COVID-19 patients that have a history of smoking in the Iranian population.

Methods

A retrospective study was conducted across six medical centers between 18 and 2020 and 15 March 2022, comprised of 678 CT scans and laboratory-confirmed COVID-19 patients that had a history of

SUBMITTER: Sharifi-Kia A 

PROVIDER: S-EPMC10360290 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature