Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption of wearable glucose monitors and mobile health applications, accurate blood glucose prediction is essential for enhancing automated insulin delivery and decision-support systems. This paper presents a deep learning-based approach for personalized blood glucose prediction, leveraging patient-specific data to improve prediction accuracy and responsiveness in real-world scenarios. Unlike traditional generalized models, our method accounts for individual variability, enabling more effective subject-specific predictions. We compare Leave-One-Subject-Out Cross-Validation with a fine-tuning strategy to evaluate their ability to model patient-specific dynamics. Results show that personalized models significantly improve the prediction of adverse events, enabling more precise and timely interventions in real-world scenarios. To assess the impact of patient-specific data, we conduct experiments comparing a multimodal, patient-specific approach against traditional CGM-only methods. Additionally, we perform an ablation study to investigate model performance with progressively smaller training sets, identifying the minimum data required for effective personalization-an essential consideration for real-world applications where extensive data collection is often challenging. Our findings underscore the potential of adaptive, personalized glucose prediction models for advancing next-generation diabetes management, particularly in wearable and mobile health platforms, enhancing consumer-oriented diabetes care solutions.

翻译：有效管理1型糖尿病需要持续血糖监测和精确的胰岛素调整，以预防高血糖和低血糖。随着可穿戴血糖监测设备和移动健康应用的日益普及，准确的血糖预测对于增强自动化胰岛素输送和决策支持系统至关重要。本文提出一种基于深度学习的个性化血糖预测方法，利用患者特异性数据提升真实场景中的预测准确性和响应能力。与传统通用模型不同，我们的方法考虑了个体差异性，能够实现更有效的特定对象预测。我们比较了留一受试者交叉验证与微调策略，以评估二者对患者特异性动态的建模能力。结果表明，个性化模型显著改善了不良事件的预测效果，使得在真实场景中能够进行更精准、更及时的干预。为评估患者特异性数据的影响，我们进行了多模态患者特异性方法与仅使用连续血糖监测的传统方法的对比实验。此外，我们通过消融实验研究了训练集规模逐步缩小时模型的性能表现，确定了实现有效个性化所需的最小数据量——这对于实际应用中常面临大规模数据收集挑战的场景至关重要。我们的研究结果凸显了自适应个性化血糖预测模型在推进下一代糖尿病管理方面的潜力，特别是在可穿戴和移动健康平台领域，有助于提升面向消费者的糖尿病护理解决方案。