ArticleBMC infectious diseases2024
A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for pneumonia diagnosis: a systematic review of multimodal learning, fusion strategies, and explainability.Frontiers in digital health · 2026Pooled it
- Methodological comparison of capillary and venous blood parameters in pediatricTranslational pediatrics · 2026Article
- Machine learning-driven risk assessment of severeFrontiers in medicine · 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
- Machine learning prediction of oxygen therapy in pediatric Mycoplasma pneumoniae pneumonia.Frontiers in digital health · 2026Article
- Development and validation of machine learning models based on blood routine tests and tumor markers in early screening of primary bronchogenic lung cancer.Translational lung cancer research · 2025Article
- Recent updates regarding the management and treatment of pneumonia in pediatric patients: a comprehensive review.Infection · 2025Review
- 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
- Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study.Journal of medical Internet research · 2025Article
- Development of machine learning-based differential diagnosis model and risk prediction model of organ damage for severe Mycoplasma pneumoniae pneumonia in children.Scientific reports · 2025Article
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8 authors.
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Abstract
backgroundThe prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predicting MPP and develop a robust and generalizable ensemble artificial intelligence (AI) model to assist in identifying patients with MPP.
methodsWe collected 27 features, including routine blood parameters and hs-CRP levels, from patients admitted to The Affiliated Dazu's Hospital of Chongqing Medical University with or without MPP between January, 2023 and January, 2024. A classification model was built using seven machine learning (ML) algorithms to develop an integrated prediction tool for diagnosing MPP. It was evaluated on both an internal validation set (982 individuals) and an external validation set (195 individuals). The primary outcome measured the accuracy of the model in predicting MPP.
resultsThe GBDT is state-of-the-art based on 27 features. Following inter-laboratory cohort testing, the GBDT demonstrated an AUC, accuracy, specificity, sensitivity, PPV, NPV, and F1-score of 0.980 (0.938-0.995), 0.928 (0.796-0.970), 0.929 (0.717-1.000), 0.926 (0.889-0.956), 0.922 (0.727-1.000), 0.937 (0.884-0.963), and 0.923 (0.800-0.966) in stratified 10-fold cross-validation. A GBDT-based AI Lab was developed to facilitate the healthcare providers in remote and impoverished areas.
conclusionsThe GBDT-based AI Lab tool, with high sensitivity and specificity, could help discriminate between pediatric MPP infection and non-MPP infection based on routine blood parameters. Moreover, a user-friendly webpage tool for AI Lab could facilitate healthcare providers in remote and impoverished areas where advanced technologies are not accessible.
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