Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.

ABSTRACT: Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its clinical features overlapping with those of other common respiratory diseases. This study aims to develop and validate machine learning (ML) models for the early identification of SMPP and the risk prediction for liver and heart damage in SMPP using accessible laboratory indicators. Cohort 1 was divided into SMPP group and other respiratory diseases group. Cohort 2 was divided into myocardial damage, liver damage, and non-damage groups. The models built using five ML algorithms were compared to screen the best algorithm and model. Receiver Operating Characteristic (ROC) curves, accuracy, sensitivity, and other performance indicators were utilized to evaluate the performance of each model. Featu

SUBMITTER: He B 

PROVIDER: S-EPMC11923196 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature