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面向个性化基底调器,用于检测1型糖尿病的基底率不准确性,而不是用餐数据:算法开发和追溯验证研究
Daniel Gasca Garcia1, Hood Thabit2,3, Paul W Nutter1
1Department of Computer Science, University of Manchester, Kilburn Building, Oxford Rd, Manchester, M13 9PL, United Kingdom, 44 7565135239.
JMIR diabetes
|November 28, 2025
概括
一个自适应算法准确地识别了1型糖尿病管理中的基数率 (BR) 不准确性,而不是用餐数据. 这种个性化的基础调器 (PBT) 改善了BR设置,降低了低血糖或高血糖的风险.
科学领域:
- 内分泌学 在内分泌学.
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 基本速率 (BR) 调整对于1型糖尿病 (T1DM) 管理至关重要,占每日胰岛素需求的30-50%.
- 当前的闭环系统在故障时恢复到手动模式 (用户通常的BR),造成风险.
- 在低收入和中等收入国家以及在没有保险的人群中,对闭环系统的访问是有限的.
- 准确的BR调整仍然具有挑战性,往往导致低血糖或高血糖.
研究的目的:
- 通过使用持续血糖监测 (CGM) 数据,提出一种适应性算法来识别BR的不准确性.
- 开发一种不需要为BR评估提供饮食摄入信息的方法.
- 与现有方法相比,评估这种新方法的实用性和精度.
主要方法:
- 使用OhioT1DM数据集进行算法实现.
- 排除了 bolus 胰岛素配置文件,缺乏餐点数据和计算的每小时血糖 (BG) 相对水平.
- 使用可靠性指标和BG相对变化,评估了与推BR的偏差.
- 使用非劣势分析和分类精度指标进行评估.
主要成果:
- 不吃饭的方法在12名参与者中有9名 (使用20%的非劣势率) 显示非劣势.
- 分类精度平均为73.9%,用于正确识别每个参与者的餐点.
- 分析显示,99.3%不受外部因素影响的BG相对值显示BR设置不正确.
结论:
- 拟议的个性化基底调器 (PBT) 算法提供了一种优越的方法,可以在没有餐点数据的情况下进行BR分析.
- 设置最佳的BR对于防止闭环系统故障期间持续的低血糖或高血糖至关重要.
- 未来的工作旨在在临床上实施这种算法,特别是在资源有限的环境中,以改善血糖控制和范围内的时间.
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