Machine learning for identifying risk of death in patients with severe fever with thrombocytopenia syndrome.

ABSTRACT:

Background

Severe fever with thrombocytopenia syndrome (SFTS) has attracted attention due to the rising incidence and high severity and mortality rates. This study aims to construct a machine learning (ML) model to identify SFTS patients at high risk of death early in hospital admission, and to provide early intensive intervention with a view to reducing the risk of death.

Methods

Data of patients hospitalized for SFTS in two hospitals were collected as training and validation sets, respectively, and six ML methods were used to construct the models using the screened variables as features. The performance of the models was comprehensively evaluated and the best model was selected for interpretation and development of an online web calculator for application.

Results

A

SUBMITTER: He Q 

PROVIDER: S-EPMC11428110 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature