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Slide 1 - Type 1 Diabetes in Engineering
Type 1 Diabetes in Engineering: Advances and Future Trends
Bridging Model-Based Control and Data-Driven Intelligence in Diabetes Management
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Engineering Advances in Type 1 Diabetes Management
Generated from prompt:
I want to create a presentatio about Type 1 diabetes in Engineering field. I want to touch model-based and data-driven approach and current trends on this topic in diabetes reseacrh. For the model base, I'm familiar (like FDA simulator, clinical trials), but i want to expand on MPC controllers. For the data driven, I'm not familiar on the current trends, but i think something like glucose forecasting (different architectures) using CGM, insulin pumps and fault detection, meal detection algorithm, hybrid close loop, advanced hybrid close loop, LLM and neuran network , digital twin (like ReplayBG), machine learning for decision support system. I also want strong references
This presentation reviews model-based control (MPC), data-driven intelligence (deep learning for glucose forecasting), hybrid systems like artificial pancreas, and emerging AI (LLMs, digital twins) for automated, personalized T1D care.
April 9, 202611 slides
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Slide 2 - Presentation Agenda
- Model-Based Approaches: Focus on MPC Controllers
- Data-Driven Approaches: Trends and Architectures
- Hybrid Systems: Artificial Pancreas and Beyond
- Advanced Intelligence: LLMs and Digital Twins
- References and Future Outlook
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Slide 3 - Model-Based Control
1
Model-Based Control in T1D
From FDA Simulators to Advanced Model Predictive Control (MPC)
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Slide 4 - Model Predictive Control (MPC) Overview
- Model Predictive Control (MPC): An optimization-based control strategy widely adopted for automated insulin delivery (AID) systems.
- Key Mechanism: Uses a mathematical model of patient glucose-insulin dynamics to predict future glucose levels and calculate optimal insulin doses.
- Advantages: Explicitly handles physiological constraints (e.g., maximum insulin delivery) and manages time-delays inherent in subcutaneous absorption.
- Current Focus: Moving towards robust MPC and personalized models that adapt to changing metabolic sensitivities.
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Slide 5 - Data-Driven Trends
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Data-Driven Trends in Diabetes Research
Harnessing CGM Data for Predictive Intelligence
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Slide 6 - Key Data-Driven Research Areas
- Glucose Forecasting: Utilizing Deep Learning architectures (LSTM, Transformer) to process multi-modal data from CGMs and insulin pumps.
- Meal Detection Algorithms: Identifying caloric intake through pattern recognition without manual entry.
- Fault Detection: Identifying sensor drift or pump malfunctions in real-time to maintain safety in closed-loop systems.
- Decision Support Systems: ML models providing actionable insights to patients for bolus calculations and lifestyle adjustments.
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Slide 7 - System Evolution: HCL to Digital Twins
Hybrid Closed-Loop (HCL) HCL systems combine algorithmic control (usually PID or MPC) with user input for meals. The 'Artificial Pancreas' standard.
Advanced HCL & Digital Twins Advanced HCL automates bolus delivery. Digital twins, like ReplayBG, simulate patient-specific physiology for scenario testing.
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Slide 8 - LLMs, Neural Networks, and Hybrid AI
- Large Language Models (LLMs): Emerging use in parsing lifestyle data and offering personalized, conversational decision support for complex T1D management.
- Neural Networks: Advanced architectures (CNNs, GRUs) applied to raw CGM time-series data for high-accuracy short-term forecasting.
- Integration: Fusing symbolic AI (physics-based models) with connectionist AI (neural networks) for 'Physics-Informed Neural Networks' (PINNs) in glucose modeling.
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Slide 9 - Summary of Research Domains
| Research Domain | Key Engineering Challenge | Current Trend/Method |
|---|---|---|
| Control Theory | System latency/safety | Adaptive/Robust MPC |
| Glucose Prediction | Non-stationary data | Transformer/LSTM models |
| Clinical Safety | Anomaly detection | Explainable AI (XAI) for monitoring |
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Slide 10 - Selected Key References
- Kovatchev, B. P., et al. (2009). The UVA/Padova Type 1 Diabetes Simulator. IEEE Transactions on Biomedical Engineering.
- Cobelli, C., et al. (2012). Artificial Pancreas: Past, Present, Future. Diabetes.
- Sparacino, G., et al. (2014). Glucose Prediction Models. IEEE Reviews in Biomedical Engineering.
- Google DeepMind/Various (2023-2024). Recent pre-prints on LLMs for Medical Decision Support (General frameworks applicable to T1D).
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Slide 11 - Conclusion
Engineering the Future of Diabetes Care
Toward Fully Automated, Safe, and Personalized T1D Management
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