Artificial intelligence for predicting mortality in hospitalized COVID-19 patients.

ABSTRACT:

Background

The global demographic situation has been significantly impacted by the COVID-19 pandemic. The objective of this study was to develop a model that predicts the risk of COVID-associated mortality using clinical and laboratory data collected within 72 h of hospital admission.

Materials and methods

A total of 3024 subjects with PCR-confirmed COVID-19 were admitted to Almazov National Research Medical Center between May 2020 and August 2021. Among them, 6.25% (n = 189) of patients had a fatal outcome. Five machine learning models and the Boruta-SHAP feature selection method were utilized to assess the risk of mortality during COVID-19 hospitalization.

Results

All methods demonstrated high efficacy, with ROC AUC (Receiver Operating Characteristic Area Und

SUBMITTER: Korsakov IN 

PROVIDER: S-EPMC11456173 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature