ArticleRespiratory research2025
Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning.
Article in Respiratory research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Association between thrombocytopenia and development of acute respiratory distress syndrome.Respiratory research · 2026Pooled it
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- S100A8/A9 as a novel predictor for severe Mycoplasma pneumoniae pneumonia in children: A prospective observational study.Clinics (Sao Paulo, Brazil) · 2026Article
- Multi-Omics Profiling Identifies Immune-Metabolic Signatures and Gut Microbial Biomarkers in a Murine Model ofBiology · 2026Article
- Mapping the Evolution of Diagnostic Research onPathogens (Basel, Switzerland) · 2026Review
- Development of an interpretable machine learning model for predicting intestinal necrosis in older patients with incarcerated hernia: A retrospective cohort study.The Journal of international medical research · 2026Article
- Prediction of severe pediatric community-acquired pneumonia using multimodal fusion of chest radiographs and clinical data.BMC pediatrics · 2026Article
- Host immune dysfunction and Mycoplasma pneumoniae immune evasion: dual drivers of pathogenesis in Mycoplasma pneumoniae pneumonia.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Review
- Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 2026Article
- A Novel Nomogram Integrating Clinical, Radiological, and Immunological Indicators for Early Prediction of SevereJournal of inflammation research · 2026Article
- Explainable machine learning for diagnosing severeFrontiers in pediatrics · 2026Article
- Machine learning-based identification of inflammatory biomarkers for predicting pulmonary consolidation in children with Chlamydia pneumoniae infection.Frontiers in pediatrics · 2026Article
- Development of a Prediction Model for Severe PediatricInfection and drug resistance · 2026Article
- Designing a broad-spectrum multi-epitope vaccine against influenza A andFrontiers in public health · 2026Article
- Machine learning prediction of oxygen therapy in pediatric Mycoplasma pneumoniae pneumonia.Frontiers in digital health · 2026Article
- Associations Between Systemic Immunity Inflammation Index, Aggregate Index of Systemic Inflammation, and Hospital Acquired Otitis Media in Children Aged ≤12 years with Bacterial Pneumonia.Infection and drug resistance · 2026Article
- Early prediction of plastic bronchitis in pediatric patients withFrontiers in cellular and infection microbiology · 2026Article
- Article
- Development and multi-database validation of interpretable machine learning models for predicting In-Hospital mortality in pneumonia patients: A comprehensive analysis across four healthcare systems.Respiratory research · 2025Article
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9 authors.
Funding
Abstract
backgroundPneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has increased in recent years. Therefore, there is an urgent need to establish an early warning model for SMPP to improve the prognosis of pediatric pneumonia.
methodsThe study comprised 597 SMPP patients aged between 1 month and 18 years. Clinical data were selected through Lasso regression analysis, followed by the application of eight machine learning algorithms to develop early warning model. The accuracy of the model was assessed using validation and prospective cohort. To facilitate clinical assessment, the study simplified the indicators and constructed visualized simplified model. The clinical applicability of the model was evaluated by DCA and CIC curve.
resultsAfter variable selection, eight machine learning models were developed using age, sex and 21 serum indicators identified as predictive factors for SMPP. A Light Gradient Boosting Machine (LightGBM) model demonstrated strong performance, achieving AUC of 0.92 for prospective validation. The SHAP analysis was utilized to screen advantageous variables, which contains of serum S100A8/A9, tracheal computed tomography (CT), retinol-binding protein(RBP), platelet larger cell ratio(P-LCR) and CD4+CD25+Treg cell counts, for constructing a simplified model (SCRPT) to improve clinical applicability. The SCRPT diagnostic model exhibited favorable diagnostic efficacy (AUC > 0.8). Additionally, the study found that S100A8/A9 outperformed clinical inflammatory markers can also differentiate the severity of MPP.
conclusionsThe SCRPT model consisting of five dominant variables (S100A8/A9, CT, RBP, PLCR and Treg cell) screened based on eight machine learning is expected to be a tool for early diagnosis of SMPP. S100A8/A9 can also be used as a biomarker for validity differentiation of SMPP when medical conditions are limited.
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