چکیده مقاله
Type 1 Diabetes T1D management requires precise insulin dosing to maintain Blood Glucose BG levels within a safe range and prevent long term complications Traditional control strategies, such as Model Predictive Control MPC , provide effective regulation but often lack adaptability to patient specific dynamics and daily variability Recently, Deep Reinforcement Learning DRL has emerged as a promising approach for personalized insulin therapy, offering the ability learn optimal policies through interaction with simulated or real environments However, the inherent black box nature of DRL limits its adoption in clinical practice, where transparency and interpretability are essential for trust and safety This study proposes an Explainable Deep Reinforcement Learning XDRL framework for insulin dosage optimization in T1D patients The framework integrates DRL with explainability techniques, including feature attribution and policy visualization, to provide clinicians with interpretable decision insights Simulation experiments conducted on the UVA/Padova T1D simulator demonstrate that the proposed XDRL approach achieves superior glycemic control compared to baseline methods, while offering transparent reasoning behind insulin dosing decisions These findings highlight the potential of XDRL to bridge the gap between advanced AI driven control strategies and clinical applicability in diabetes management
کلیدواژهها
نویسندگان
Sayna Davoodi
Department of Electrical Engineering, Shahid Beheshti University, Tehran, Iran
شیوه ارجاع
Davoodi, Sayna,1404,Explainable Deep Reinforcement Learning for Optimizing Insulin Dosage in Type-1 Diabetes Patients,22th International Conference on Innovation and Research in Engineering Sciences (ICIRES)
ارائهشده در
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