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Improving IV Insulin Administration in a Community Hospital
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Explainable Meta-Learning Ensemble Framework for Predicting Insulin Dose Adjustments in Diabetic Patients: A

Emek Guldogan1, Burak Yagin1, Hasan Ucuzal1

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Türkiye.

Medicina (Kaunas, Lithuania)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study developed an explainable AI framework using Meta-Learning Ensemble for accurate insulin dose adjustments in diabetes management. The model enhances clinical decision-making by providing interpretable predictions, improving patient outcomes.

Keywords:
LIMESHAPclinical decision supportdiabetes mellitusensemble methodsexplainable artificial intelligencegradient boostinginsulin dose predictionmachine learningmeta-learning

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Area of Science:

  • Artificial Intelligence in Medicine
  • Machine Learning for Clinical Decision Support
  • Diabetes Mellitus Management

Background:

  • Diabetes mellitus is a global health challenge requiring precise insulin management to prevent complications.
  • Optimizing insulin dosage is complex, with risks of hypoglycemia and hyperglycemia from incorrect dosing.
  • Machine learning offers potential for clinical decision support but needs high accuracy and interpretability.

Purpose of the Study:

  • To develop and evaluate an explainable machine learning framework for predicting insulin dose adjustments.
  • To compare ensemble learning approaches for balancing predictive performance and clinical interpretability.
  • To utilize SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for model transparency.

Main Methods:

  • Utilized a dataset of 10,000 patient records with 12 clinical/demographic features.
  • Implemented and compared nine machine learning models, including gradient boosting variants and ensemble strategies (Voting, Stacking, Blending, Meta-Learning).
  • Evaluated models using accuracy, F1-score, AUC-ROC, PR-AUC, sensitivity, specificity, and cross-entropy loss, with SHAP and LIME for interpretability.

Main Results:

  • The Meta-Learning Ensemble achieved superior performance (81.35% accuracy, 0.9637 AUC-ROC, 0.9317 PR-AUC).
  • Demonstrated high sensitivity (86.61%) and specificity (91.79%), with perfect sensitivity for dose reduction detection.
  • SHAP analysis identified insulin sensitivity, medications, sleep, weight, and BMI as key predictors; LightGBM probability estimates were crucial for the ensemble.

Conclusions:

  • The explainable Meta-Learning Ensemble framework effectively predicts insulin dose adjustments with robust interpretability.
  • SHAP explanations enhance clinician trust and understanding, supporting informed diabetes management decisions.
  • This approach advances the clinical application of AI for personalized insulin therapy.