Application of decision tree model in diagnosis of mycoplasma pneumoniae pneumonia with plastic bronchitis

Background To 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. Methods A retrospective study was conducted to collect the cl...

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Vydáno v:Italian journal of pediatrics Ročník 51; číslo 1; s. 95 - 7
Hlavní autoři: Li, Lin, Wang, Dong, Yang, Rongrong, Liao, Xing, Wu, Ling
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 24.03.2025
Springer Nature B.V
BMC
Témata:
Analytical chemistry
Bronchitis
Bronchitis - complications
Bronchitis - diagnosis
Bronchitis - microbiology
Bronchoscopy
C-reactive protein
Child
Child, Preschool
Childhood pneumonia
Children
Childrens health
Classification
Datasets
Decision making
Decision tree model
Decision Trees
Dehydrogenases
Diagnosis
Disease
Drug resistance
Female
Ferritin
Fever
Humans
Infections
Infectious Diseases and Vaccinology
Kinases
Male
Maternal and Child Health
Medicine
Medicine & Public Health
Mycoplasma pneumoniae
Normal distribution
Pediatrics
Plastic bronchitis
Plastics
Pleural effusion
Pneumonia
Pneumonia, Mycoplasma - complications
Pneumonia, Mycoplasma - diagnosis
Retrospective Studies
Statistical analysis
Statistical models
Tuberculosis
Mycoplasma pneumoniae
Childhood pneumonia
Diagnosis
Plastic bronchitis
Decision tree model
ISSN:1824-7288, 1720-8424, 1824-7288
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Shrnutí:Background To 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. Methods A 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. Result In 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. Conclusion Decision 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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1824-7288
1720-8424
1824-7288
DOI:10.1186/s13052-025-01934-8