ArticleScientific reports2025
Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.
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 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based prediction models for severeFrontiers in public health · 2026Pooled it
- Research progress on detection technologies for Mycoplasma pneumoniae.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Early laboratory-based differentiation of severe influenza pneumonia and influenza-associated encephalopathy in children using a multi-model, stability-aware machine-learning workflow: a single-center retrospective cohort study.BMC infectious diseases · 2026Article
- Correlation analysis of FABP3, MCP-4, and CXCL9 levels and myocardial damage in patients with severe pneumonia.Journal of medical biochemistry · 2026Article
- Development of a nomogram to predict acute liver injury in children withFrontiers in pediatrics · 2026Article
- Development of a Prediction Model for Severe PediatricInfection and drug resistance · 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
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
- A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.Scientific reports · 2025Article
- Predicting and interpreting key features of refractory Mycoplasma pneumoniae pneumonia using multiple machine learning methods.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
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. Feature importance and Shapley Additive Explanation (SHAP) values were introduced to enhance the interpretability of models. Cohort 3 was used for external validation. In Cohort 1, the SMPP differential diagnostic model developed using the LightGBM algorithm achieved the highest performance with AUC
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