ArticleBMC infectious diseases2025
A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Development and validation of a machine learning-driven framework for differentiating pediatric bronchopneumonia from lobar pneumonia: a multicenter investigation.European journal of medical research · 2025Trial
- Mapping the Evolution of Diagnostic Research onPathogens (Basel, Switzerland) · 2026Review
- Prediction of severe pediatric community-acquired pneumonia using multimodal fusion of chest radiographs and clinical data.BMC pediatrics · 2026Article
- Methodological comparison of capillary and venous blood parameters in pediatricTranslational pediatrics · 2026Article
- Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.Current opinion in pediatrics · 2026Review
- SHAP based feature selection for machine learning prediction of mycoplasma pneumoniae pneumonia with atelectasis in children.BMC pediatrics · 2026Article
- Blood-based inflammatory parameters and machine learning models for mortality prediction in critically ill pediatric pneumonia: a retrospective cohort study.Translational pediatrics · 2026Article
- Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- Nomogram to predict severeFrontiers in pediatrics · 2026Article
- Immunopathogenesis, molecular phenotyping, and host-directed interventions for severe pediatricFrontiers in cellular and infection microbiology · 2026Review
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- Explainable machine learning for diagnosing severeFrontiers in pediatrics · 2026Article
- Development and Validation of a Nomogram Model to Predict the Risk of Severe Pneumonia in Children with Pneumococcal Infection.Infection and drug resistance · 2026Article
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
- Development and validation of a nomogram for early prediction of macrolide-unresponsive Mycoplasma pneumoniae pneumonia in children.Frontiers in pediatrics · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.
methodsWe collected clinical data from 483 school-aged children with M. pneumoniae pneumonia (MPP) who were hospitalized at the Children's Hospital of Soochow University between September 2021 and June 2024. Difference analysis and univariate logistic regression were employed to identify predictors for training features in ML. Eight ML algorithms were used to build models based on the selected features, and their effectiveness was validated. The area under the curve (AUC), accuracy, five-fold cross-validation, and decision curve analysis (DCA) were utilized to evaluate model performance. Finally, the best-performing ML model was selected, and the Shapley Additive Explanations (SHAP) method was applied to rank the importance of clinical features and interpret the final model.
resultsAfter feature selection, 30 variables remained. We constructed eight ML models and assessed their effectiveness, finding that the CatBoost model exhibited the best predictive performance, with an AUC of 0.934 and an accuracy of 0.9175. DCA was used to compare the clinical benefits of the models, revealing that the CatBoost model provided greater net benefits than the other ML models within the threshold probability range of 34% to 75%. Additionally, we applied the SHAP method to interpret the CatBoost model, and the SHAP diagram was used to visually show the influence of predictor variables on the outcome. The results identified the top six risk factors as the number of days with fever, D-dimer, platelet count (PLT), C-reactive protein (CRP), lactate dehydrogenase (LDH), and the neutrophil-to-lymphocyte ratio (NLR).
conclusionsThe interpretable CatBoost model can help physicians accurately identify school-aged children with SMPP. This early identification facilitates better treatment options and timely prevention of complications. Furthermore, the SHAP algorithm enhances the model's transparency and increases its trustworthiness in practical applications.
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