使用UVA重放模拟技术的自动化胰岛素输送系统参数优化策略的硅验证

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.06.022

María F. Villa-Tamayo (Ms) , Jacopo Pavan (PhD) , Marc Breton (PhD)

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引用次数: 0

摘要

自动化胰岛素输送（AID）系统在治疗1型糖尿病（T1D）方面显示出巨大的潜力，但个性化治疗参数仍然具有挑战性。本研究通过利用弗吉尼亚大学重播模拟器（UVA-RS）的更新决策支持系统（DSS），推进了AID治疗方案的优化。DSS采用个性化的葡萄糖-胰岛素动力学模型来模拟葡萄糖对治疗调整的反应，并采用优化算法来确定改善整体血糖控制的治疗参数。我们通过三个计算机场景评估了系统的性能，重点关注推荐可靠性、约束影响、对代谢和行为可变性的鲁棒性以及五个月模拟使用的性能。结果表明，改善治疗个性化和血糖控制，支持DSS提高AID系统疗效的潜力。

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In-Silico Validation of Parameter Optimization Strategies for Automated Insulin Delivery Systems using the UVA Replay Simulation Technology

Automated insulin delivery (AID) systems have shown significant potential in managing type 1 diabetes (T1D), yet personalizing therapy parameters remains challenging. This study advances the optimization of AID therapy profiles through an updated decision support system (DSS) leveraging the University of Virginia Replay Simulator (UVA-RS). The DSS employs a personalized glucose-insulin dynamics model to simulate glucose response to therapy adjustments and an optimization algorithm to determine therapy parameters that improves overall glycemic control. We evaluated the system’s performance through three in-silico scenarios, focusing on recommendation reliability, constraint impact, robustness to metabolic and behavioral variability, and performance over five-month simulated use. Results indicate improved therapy personalization and glycemic control, supporting the potential for DSS to enhance AID system efficacy.

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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