Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis.

ABSTRACT:

Background and objectives

We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19.

Methods

A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a

SUBMITTER: Padilha DMH 

PROVIDER: S-EPMC9715454 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature