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Development of a Machine Learning-based Model for Methimazole Dosage Adjustment in Youth With Hyperthyroidism.

Joon Young Kim1, Kanghyuck Lee2, Eunsik Choi3

  • 1Department of Pediatrics, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul 06273, Republic of Korea.

The Journal of Clinical Endocrinology and Metabolism
|October 8, 2025
PubMed
Summary

Machine learning models can now predict optimal methimazole (MMI) dosage for pediatric hyperthyroidism, improving clinical efficiency. The XGBoost model showed the best performance in predicting MMI doses for children.

Keywords:
drug dosage calculationshyperthyroidismmachine learningmethimazolepediatrics

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Area of Science:

  • Endocrinology
  • Pediatric Medicine
  • Computational Biology

Background:

  • Accurate methimazole (MMI) dosage is critical for pediatric hyperthyroidism, but current titration relies on clinical expertise due to a lack of predictive tools.
  • Individualized MMI dosing requires validated methods to optimize treatment outcomes in children.

Purpose of the Study:

  • To develop and validate machine learning (ML) models for predicting optimal MMI dosage in pediatric hyperthyroidism.
  • To establish a data-driven approach for MMI dose adjustment in young patients.

Main Methods:

  • A retrospective, multicenter study involving ML models (linear regression, decision tree, support vector regression, XGBoost, feed-forward neural networks).
  • Models were trained on data from 1,512 visits and validated using two external cohorts (666 and 31 visits).
  • Predictors included age, sex, anthropometrics, prior MMI dose, treatment duration, and thyroid function tests. Performance was measured by Mean Absolute Error (MAE).

Main Results:

  • The eXtreme Gradient Boosting (XGBoost) model achieved the best performance with MAE of 1.72 mg (internal validation) and 1.08 mg (external validation).
  • Shapley Additive Explanations (SHAP) analysis identified previous MMI dose, triiodothyronine, and free thyroxine levels as key predictors.

Conclusions:

  • This study presents the first data-driven tool to guide methimazole dosing in pediatric hyperthyroidism.
  • The developed ML models can enhance clinical efficiency and support individualized MMI titration for children.