Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.

ABSTRACT:

Objective

This investigation aims to establish and substantiate a machine learning-driven predictive framework designed to precisely distinguish between pediatric bronchopneumonia and lobar pneumonia. This endeavor seeks to elevate the accuracy of early clinical support, refine treatment decision-making, and curtail superfluous medical interventions.

Methods

This study was executed at Siyang Hospital, enrolling 2304 pediatric patients diagnosed with either bronchopneumonia or lobar pneumonia from January 2020 to December 2024. Participants were randomized in a 7:3 ratio into training (n = 1612) and testing (n = 692) sets, supplemented by an external validation set (n = 454) to evaluate the model's generalizability. Hematological and serum biochemical parameters were gathered

SUBMITTER: Cai Z 

PROVIDER: S-EPMC12837292 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature