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

Asiful Arefeen, Saman Khamesian, Maria Adela Grando, Bithika Thompson, Hassan Ghasemzadeh

TL;DR

GlyTwin is a novel digital twin framework that uses counterfactual explanations to simulate optimal treatments for glucose regulation and outperforms state-of-the-art counterfactual methods, generating 76.6% valid and 86% effective interventions.

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.

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

TL;DR

GlyTwin is a novel digital twin framework that uses counterfactual explanations to simulate optimal treatments for glucose regulation and outperforms state-of-the-art counterfactual methods, generating 76.6% valid and 86% effective interventions.

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.

Paper Structure

This paper contains 38 sections, 12 equations, 10 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Results
  2. Data Collection
  3. Classifier Performance
  4. Quality of the counterfactuals
  5. Classifying the results
  6. Diversity of the counterfactuals
  7. Alignment with the preference weights
  8. Discussion
  9. Impact of target probability
  10. Impact of perturbation size $\delta$
  11. How long does GlyTwin take?
  12. Limitations of GlyTwin
  13. Clinical validation of the interventions
  14. Suggested $\Delta t$ values
  15. Higher computation cost
  16. ...and 23 more sections

Figures (10)

  • Figure 1: Digital twin framework with enhanced capabilities that can model physiological response, simulate treatments, and identify optimal treatment.
  • Figure 2: Counterfactual XAI for hyperglycemia prevention.
  • Figure 3: GlyTwin framework consists of four phases: data acquisition from CGM sensor and insulin logs, model training for glycemic outcome prediction, counterfactual generation for actionable recommendations, and integration into a dynamic, personalized management pipeline.
  • Figure 4: Categorizing the performance of GlyTwin based on patient age, sex, A1C and years from diagnosis (YfD).
  • Figure 5: Comparison of feature diversity among CFs produced using different techniques.
  • ...and 5 more figures