Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.

ABSTRACT:

Background

Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate transmission and avert complications promptly is imperative. Nevertheless, the current diagnostic methods, including PCR and bacterial culture, are time-consuming and expensive. Some studies have attempted to develop risk prediction models based on multivariate data, but their performance can be improved. Therefore, this study aims to further optimize and expand the risk assessment tool to more efficiently identify high-risk individuals and compensate for the shortcomings of existing diagnostic methods.

Objective

The aim of this

SUBMITTER: Xie J 

PROVIDER: S-EPMC11951648 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature