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Electroacupuncture Combined with Chinese Medicine Ironing Therapy for Functional Constipation
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Subtyping intractable functional constipation in children using clinical and laboratory data in a classification

Yi-Hsuan Huang1,2, Chenjia Xie3, Chih-Yi Chou4

  • 1Department of Gastroenterology, Children's Hospital of Nanjing Medical University, Nanjing, China.

Frontiers in Pediatrics
|May 11, 2023
PubMed
Summary

Machine learning accurately classifies pediatric intractable functional constipation (IFC) subtypes using questionnaires and hormone levels. The Light Gradient Boosting Machine (LGBM) model achieved 83.8% accuracy, aiding in early management of IFC in children.

Keywords:
childrenconstipation subtypesintractable constipationmachine learningquality of life

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Area of Science:

  • Pediatric Gastroenterology
  • Biomedical Informatics
  • Machine Learning Applications

Background:

  • Intractable functional constipation (IFC) in children leads to severe symptoms and reduced quality of life, persisting into adulthood.
  • Early and efficient subtyping of IFC into normal transit constipation (NTC), outlet obstruction constipation (OOC), and slow transit constipation (STC) is crucial for effective management.
  • Machine learning offers a promising approach for early IFC subtyping using validated questionnaires and serum gastrointestinal hormone concentrations.

Purpose of the Study:

  • To evaluate the efficacy of supervised machine learning models in classifying pediatric IFC subtypes.
  • To identify key clinical and biochemical variables for accurate IFC subtyping.
  • To compare the performance of different machine learning models for IFC classification.

Main Methods:

  • 101 children with IFC and 50 controls were analyzed.
  • Supervised machine learning models (SVM, Random Forest, LGBM) were employed to classify IFC subtypes.
  • Classification was based on symptom severity, quality of life, self-efficacy (questionnaires), and serum gastrointestinal hormone levels (ELISA).
  • Model accuracy was validated against radiopaque marker results.

Main Results:

  • The Light Gradient Boosting Machine (LGBM) model achieved the highest accuracy (83.8%) in classifying IFC subtypes.
  • Significant variables for classification included stool frequency, PAC-QOL satisfaction, SEFCQ emotional self-efficacy, motilin, and vasoactive intestinal peptide serum concentrations.
  • The LGBM model demonstrated high performance with precision (84.5%), recall (83.6%), F1-score (83.4%), and AUROC (0.89).

Conclusions:

  • Machine learning models, particularly LGBM, can efficiently classify pediatric IFC subtypes using clinical data and serum hormone levels.
  • This approach facilitates early and accurate diagnosis, improving the management of IFC in children.
  • Machine learning serves as a valuable tool for personalized treatment strategies in pediatric functional constipation.