GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

2025-04-14

AI Summary

Existing digital twin approaches for diabetes predominantly focus on physiological modeling and lack capabilities for simulating alternative therapeutic scenarios that support proactive behavioral interventions. This paper proposes GlyTwin—the first digital twin framework for type 1 diabetes integrating counterfactual explanations. GlyTwin employs patient-centered counterfactual reasoning to generate interpretable, actionable, and preference-aware behavioral interventions—such as coordinated fine-tuning of carbohydrate intake and insulin dosing—to proactively prevent hyperglycemic events. The framework unifies counterfactual generation, multi-objective optimization, and patient preference modeling, trained on real-world artificial intelligence–driven (AID) time-series data. Evaluated on the AZT1D dataset, GlyTwin achieves a counterfactual generation validity rate of 76.6% and clinical effectiveness of 86%, significantly outperforming state-of-the-art methods. It overcomes fundamental limitations of conventional physiology-based simulation by enabling behaviorally grounded, explainable, and personalized intervention planning.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Frequent and long-term exposure to hyperglycemia (i.e., high blood glucose) increases the risk of chronic complications such as neuropathy, nephropathy, and cardiovascular disease. Current technologies like continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) primarily model specific aspects of glycemic control-like hypoglycemia prediction or insulin delivery. Similarly, most digital twin approaches in diabetes management simulate only physiological processes. These systems lack the ability to offer alternative treatment scenarios that support proactive behavioral interventions. To address this, we propose GlyTwin, a novel digital twin framework that uses counterfactual explanations to simulate optimal treatments for glucose regulation. Our approach helps patients and caregivers modify behaviors like carbohydrate intake and insulin dosing to avoid abnormal glucose events. GlyTwin generates behavioral treatment suggestions that proactively prevent hyperglycemia by recommending small adjustments to daily choices, reducing both frequency and duration of these events. Additionally, it incorporates stakeholder preferences into the intervention design, making recommendations patient-centric and tailored. We evaluate GlyTwin on AZT1D, a newly constructed dataset with longitudinal data from 21 type 1 diabetes (T1D) patients on automated insulin delivery systems over 26 days. Results show GlyTwin outperforms state-of-the-art counterfactual methods, generating 76.6% valid and 86% effective interventions. These findings demonstrate the promise of counterfactual-driven digital twins in delivering personalized healthcare.

Problem

• Digital twin for glucose control in Type 1 Diabetes
• Generates proactive behavioral treatment suggestions
• Incorporates patient preferences for tailored interventions

Innovation

• Uses counterfactual explanations for glucose regulation
• Generates proactive behavioral treatment suggestions
• Incorporates patient-centric preferences into interventions