Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality.

ABSTRACT: As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distributio

SUBMITTER: Sun Y 

PROVIDER: S-EPMC10161188 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature