在HUPA-UCM糖尿病数据集中,
J Ignacio Hidalgo1, Jorge Alvarado2, Marta Botella3
1Universidad Complutense de Madrid, Profesor José García Santesmases 9, Madrid, Spain.
Data in brief
|July 1, 2024
概括
这一数据集为1型糖尿病管理提供了有价值的见解. 它包括持续的葡萄糖监测,胰岛素和生活方式数据,以开发葡萄糖水平和睡眠影响的预测模型.
科学领域:
- 生物医学信息学 生物医学信息学
- 内分泌学 在内分泌学.
- 数据科学数据科学数据科学
背景情况:
- 1型糖尿病 (T1DM) 的管理需要不断监测葡萄糖水平,并了解影响因素.
- 整合生理和生活方式数据可以增强T1DM的预测建模.
研究的目的:
- 为T1DM研究提供全面的数据集,包括持续血糖监测 (CGM),胰岛素,营养和活动数据.
- 促进T1DM患者葡萄糖波动,低血糖和高血糖的预测模型的开发.
- 研究T1DM中睡眠模式和血糖控制之间的复杂关系.
主要方法:
- 在至少14天内收集了25名T1DM患者的数据.
- 使用FreeStyle Libre 2 CGMs进行血糖读数和Fitbit Ionic智能手表进行活动和睡眠跟踪.
- 记录的胰岛素剂量和食碳水化合物摄入量 (克).
主要成果:
- 该数据集包括同步的CGM,胰岛素,餐,步骤,卡路里,心率和睡眠数据.
- 这些数据使人们能够探索生活方式因素,睡眠和血糖变化之间的相关性.
- 以前的分析已经证明了这个数据集对基于机器学习的葡萄糖预测的有用性.
结论:
- 这一数据集为推进T1DM研究和个性化管理策略提供了丰富的资源.
- 它支持对血糖控制和确定关键影响变量的复杂预测模型的开发.
- 使用此数据集进行进一步的研究可以阐明睡眠对T1DM血糖结果的影响.
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