ArticleScientific reports2025
Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- SHAP based feature selection for machine learning prediction of mycoplasma pneumoniae pneumonia with atelectasis in children.BMC pediatrics · 2026Article
- A rare case of lung abscess caused by Mycoplasma pneumoniae in an adult with chronic obstructive pulmonary disease.BMC infectious diseases · 2026Article
- A Predictive Nomogram for Severe RSV Infection in Children: A Retrospective, Single-Center Development and Validation Study.Journal of inflammation research · 2026Article
- Explainable machine learning for diagnosing severeFrontiers in pediatrics · 2026Article
- Development of a predictive model based on clinical indicators for refractory Mycoplasma pneumoniae pneumonia in children: a case-control study.Frontiers in cellular and infection microbiology · 2026Article
- A Novel Nomogram Integrating Clinical, Radiological, and Immunological Indicators for Early Prediction of SevereJournal of inflammation research · 2026Article
- Machine learning prediction of oxygen therapy in pediatric Mycoplasma pneumoniae pneumonia.Frontiers in digital health · 2026Article
- From genes to clinical application: a circulating four-gene signature for early diagnosis model of refractoryFrontiers in cellular and infection microbiology · 2026Article
- Profound immune suppression and exhaustion characterize refractory mycoplasma pneumoniae pneumonia in children.Frontiers in immunology · 2026Article
- Pathogen prevalence, feature composition and cross-centre generalisability of machine learning diagnostic models for multi-pathogen respiratory infection.Frontiers in public health · 2026Article
- A consensus-weighted multi-agent ensemble feature selection framework for antibiotic susceptibility prediction in pediatric respiratory diseases.Frontiers in artificial intelligence · 2026Article
- Screening of early predictive serum biomarkers and construction of a combined predictive model for refractoryFrontiers in pediatrics · 2026Article
- Pulmonary lesion volume ratio and laboratory parameters as risk factors for plastic bronchitis in pediatric refractory Mycoplasma pneumoniae pneumonia.BMC pediatrics · 2025Article
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
- A Novel Knowledge Fusion Ensemble for Diagnostic Differentiation of Pediatric Pneumonia and Acute Bronchitis.Diagnostics (Basel, Switzerland) · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
In recent years, the incidence of refractory Mycoplasma pneumoniae pneumonia (RMPP) has significantly risen, posing severe pulmonary and extrapulmonary complications, making early identification a challenge for clinicians. In this retrospective single-center study, we included patients diagnosed with Mycoplasma pneumoniae pneumonia in 2021, categorizing them into RMPP and non-RMPP groups. Univariate regression analysis initially identified variables associated with RMPP. Seven mainstream machine learning methods were then employed to construct predictive models, evaluated for reliability and robustness through tenfold cross-validation and sensitivity analysis. Ultimately, the optimal predictive model was selected using multidimensional metric assessments, and SHAP analysis identified key predictive factors related to RMPP. Twenty-nine factors from various dimensions were found to be associated with RMPP and used to build the predictive model. The XGBoost model demonstrated high predictive capability with an accuracy of 0.80 and an AUC of 0.93. Ten-fold cross-validation and sensitivity analysis confirmed the model's robustness and reliability. SHAP analysis interpreted the final model with 8 key features. These features include fever duration, macrolide treatment before hospitalization, severe Mycoplasma pneumoniae pneumonia, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, alanine aminotransferase, peak fever, and extensive lung consolidation. This simple, effective predictive model enhances clinicians' understanding and aids early identification of RMPP.
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