ArticleItalian journal of pediatrics2025
Application of decision tree model in diagnosis of mycoplasma pneumoniae pneumonia with plastic bronchitis.
Article in Italian journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Various machine learning prediction models for short-term postoperative prognosis of colorectal cancer based on intraoperative hypothermia.American journal of cancer research · 2026Article
- Early prediction of plastic bronchitis in pediatric patients withFrontiers in cellular and infection microbiology · 2026Article
- Clinical features and risk factors of plastic bronchitis in Mycoplasma pneumoniae pneumonia children with pulmonary consolidation: a prospective cohort study.Frontiers in pediatrics · 2026Article
- Clinical risk factors and a predictive nomogram for mechanical ventilation in pediatric patients with infection-related plastic bronchitis.BMC infectious diseases · 2025Article
- A machine learning-based predictive model for multilobar pulmonary consolidation induced by macrolide-resistantMicrobiology spectrum · 2025Article
- Development and validation of a CD4+/CD8+ ratio-based nomogram to predict plastic bronchitis in pediatricFrontiers in pediatrics · 2025Article
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5 authors.
Funding
Abstract
backgroundTo establish a decision tree model of Mycoplasma pneumoniae pneumonia(MPP) complicated with plastic bronchitis(PB) in children, and to explore the application value of decision tree model in the auxiliary diagnosis of children.
methodsA retrospective study was conducted to collect the clinical data of 214 children who met the admission criteria in Fujian Children's Hospital from June 2022 to June 2024, and they were divided into plastic bronchitis group (n = 66) and non-plastic bronchitis group (n = 148). Using R language, 70% of the data from each group of patients was randomly selected for training the model using decision tree algorithm analysis, thus generating a clinical diagnostic decision tree for Mycoplasma pneumoniae (MP) combined with PB. The generated decision tree model was validated on the validation sample dataset and the detection effect value of the model was calculated.
resultIn this study, a total of 22 indicators were employed to build the decision tree diagnostic model. Univariate statistical analysis was carried out prior to the model construction, and it was discovered that the differences of 13 indicators between the molded group and the non-molded group were statistically significant. A decision tree model with D-dimer ≥ 1.7ug/mL, C-reactive protein ≥ 15 mg/L, drug resistance or not, and serum ferritin<137 mg/L was constructed in the training sample dataset of the molded group and the non-molded group. The sensitivity of the decision tree model was 0.884, which was verified in the dataset of the remolded group and the non-molded group. The specificity was 0.727, and the area under the receiver operating characteristic curve was 0.831.
conclusionDecision tree model can provide reference for the application of auxiliary diagnosis in children with mycoplasma pneumoniae pneumonia complicated with plastic bronchitis. The model has good discriminative ability in general, and is worthy of clinical application and further study.
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