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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication

Manaf Zargoush1, Somayeh Ghazalbash2, Mahsa Madani Hosseini3

  • 1Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, ON, Canada. zargoush@mcmaster.ca.

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|July 23, 2025
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Summary
This summary is machine-generated.

Machine learning (ML) can personalize type 2 diabetes (T2D) treatment via a novel clinical decision support system (CDSS). The framework optimizes medication prescriptions, showing promise for improved patient outcomes despite challenges in complex cases.

Keywords:
Bayesian networkData-driven optimizationMachine learningPersonalized medicinePredictive-prescriptive analyticsType 2 diabetes

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Type 2 Diabetes (T2D) prevalence necessitates personalized treatment strategies.
  • Machine learning (ML) offers potential for data-driven clinical decision support systems (CDSS).
  • Current treatment approaches require enhancement for individual patient profiles.

Purpose of the Study:

  • Develop a novel predictive-prescriptive analytics framework using ML for enhanced T2D medication prescriptions.
  • Create a data-driven CDSS to tailor treatment strategies based on patient-specific data.
  • Improve clinical decision-making for T2D management.

Main Methods:

  • Utilized a comprehensive electronic health records dataset (17,773 patients, 12 years).
  • Employed Bayesian Networks (BN) for their dual predictive and prescriptive capabilities.
  • Applied rule-based and decision-tree methods for pathway illustration and interpretability.

Main Results:

  • ML demonstrated strong predictive performance (Precision: 0.789, Recall: 0.879, F1-score: 0.831).
  • Alignment between ML recommendations and physician prescriptions was high in simple scenarios.
  • Alignment decreased with increased prescription complexity, indicating challenges in complex combination therapy.

Conclusions:

  • A novel ML-based CDSS framework for personalized T2D treatment was developed.
  • The framework shows promise for optimizing medication prescriptions and improving patient outcomes.
  • Enhanced CDSS are crucial for complex treatment scenarios and physician compliance.