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Machine learning-based prediction models for severe Mycoplasma pneumoniae pneumonia in Chinese children: a systematic
Juan Cao1, Jiao Nie2, Danxia Wu3
1College of Medicine and Health Sciences, China Three Gorges University, Yichang, Hubei, China.
Insights
Machine learning models show promise in predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in children, achieving high accuracy. However, further research is needed to refine these models for clinical use.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Biostatistics
Background:
- Severe Mycoplasma pneumoniae pneumonia (SMPP) poses a significant health risk to pediatric patients.
- Accurate prediction of SMPP progression is crucial for timely intervention and improved patient outcomes.
- Existing prediction models require systematic evaluation of their performance and methodological rigor.
Purpose of the Study:
- To systematically evaluate the predictive performance of machine learning (ML) models for SMPP in children.
- To assess the methodological characteristics and quality of ML-based SMPP prediction models.
- To identify key predictors and modeling approaches for SMPP in pediatric populations.
Main Methods:
- A comprehensive literature search was conducted across multiple databases (PubMed, EMBASE, Web of Science, etc.) up to November 2025.
- Included studies developing or validating SMPP prediction models in children.
- Data extraction focused on study characteristics, algorithms, predictors, and performance metrics (AUC); methodological quality was assessed using PROBAST.
Main Results:
- Thirteen studies were included, with reported AUC values for predictive models ranging from 0.81 to 0.90.
- Machine learning models, including XGBoost and random forest, generally demonstrated higher AUC values compared to other approaches.
- Common predictors identified were age, insulin use, BMI, HbA1c, creatinine, and history of hypoglycemia.
Conclusions:
- Current research on SMPP risk prediction models in children is in its early stages.
- While models show high discriminatory performance, methodological limitations and challenges in clinical translation persist.
- Future research should prioritize developing robust, interpretable ML models for pediatric clinical practice.
Objectives:
This study aimed to systematically evaluate the predictive performance and methodological characteristics of machine learning-based models for predicting progression to severe Mycoplasma pneumoniae pneumonia (SMPP) in pediatric patients.
Methods:
A comprehensive literature search was conducted in PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, and Wanfang databases from inception to November 2025 to identify studies developing or validating prediction models for SMPP in children. Data on study characteristics, modeling algorithms, predictors, and performance metrics were extracted. A narrative synthesis was performed to summarize model characteristics, predictors, and modeling approaches, while model discrimination was quantitatively synthesized using pooled area under the receiver operating characteristic curve (AUC). Subgroup analyses were conducted according to modeling algorithms. Methodological quality and risk of bias were assessed using the PROBAST tool.
Results:
A total of 13 studies were included. The reported prevalence of hypoglycemia ranged from 17 to 33%. The AUC for predictive models ranged from 0.81 to 0.90. Subgroup analyses showed that machine learning-based models such as XGBoost and random forest generally reported higher AUC values compared with other modeling approaches. Commonly reported predictors included age, insulin use, body mass index, HbA1c, creatinine, and history of hypoglycemia.
Conclusion:
Research on risk prediction models for SMPP in children is still at a developmental stage. Although current models demonstrate high discriminatory performance, methodological limitations and limited clinical translation remain. Future studies should focus on developing robust, interpretable machine learning models and facilitating their integration into pediatric clinical practice.
Systematic Review Registration:
https://www.crd.york.ac.uk/PROSPERO/ CRD42020190338.
