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Updated: Jul 15, 2025

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在开源自动化胰岛素输送系统的两个大规模和现实世界的数据集中分析葡萄糖变化
Drew Cooper1, Bernd Reinhold2, Arsalan Shahid3
1Institute of Medical Informatics, Charité-Universitätsmedizin Berlin, Berlin, Germany.
Journal of diabetes science and technology
|September 26, 2023
概括
开源自动化胰岛素输送 (OS-AID) 系统在治疗糖尿病方面证明了现实世界的有效性. 对用户数据的分析表明,这些系统有助于实现推的血糖指数目标,改善了范围内的时间.
科学领域:
- 内分泌学和糖尿病技术 技术
- 生物医学数据科学 生物医学数据科学
- 医疗保健中的人工智能
背景情况:
- 开源自动化胰岛素输送 (OS-AID) 系统集成商业胰岛素和连续血糖监测器与开源算法.
- 这些系统旨在为患有需要胰岛素的糖尿病患者自动注射胰岛素.
- 来自OPEN和OpenAPS Data Commons两个数据集的匿名用户数据被用于分析.
研究的目的:
- 评估OPEN数据集中的血糖变异性 (GV) 结果.
- 为了比较OPEN数据集和OpenAPS数据共享的子集之间的GV指标.
- 量化分析人口统计数据和GV指标.
主要方法:
- 使用OPEN数据集评估了血糖变化 (GV) 结果.
- 无监督的机器学习算法用于葡萄糖数据集群.
- 使用统计测试来量化GV指标,并比较数据集之间的分布.
主要成果:
- 开放式数据集 (n=75) 包含36,827天的数据,平均时间范围 (TIR) 为82.08%.
- 低血糖指数 (LBGI) 根据性别显著不同 (P < .05),而高血糖指数 (HBGI) 的分布相似.
- 大多数GV指标显示了OPEN和OpenAPS数据共享数据集之间的统计学上显著差异 (P < .05).
结论:
- 无论是OPEN还是OpenAPS数据共享数据集都表明,OS-AID系统在70以下的时间 (TOR < 70),TIR和180以上的时间 (TOR > 180) 中实现了推的目标.
- 这进一步证明了OS-AID的实际有效性.
- 未来的研究应该探索数据集的变化以及用户行为模式与GV结果之间的联系.
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