The development and validation of a privacy-preserving model based on federated learning for diagnosing severe pediatric pneumonia.

ABSTRACT:

Background

There is a challenge of in diagnostic testing of pneumonia in children, especially severe pneumonia. Thus, developing an auxiliary diagnostic model to help identify severe pneumonia in pediatric patients at an early stage would be highly valuable to address the issues. To overcome the issue of privacy protection, we applied a privacy-preserving machine learning framework to build a multicenter diagnostic model based on federated learning technology.

Methods

Based on Arya, a novel privacy computing platform developed by Hangzhou Healink Technology Corporation, several privacy-preserving federated learning models were developed using datasets from one, two, or four medical centers. A total of 5,091 records were included in this multicenter retrospective study, with

SUBMITTER: Wang D 

PROVIDER: S-EPMC12268547 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature