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通过数据增强模拟现实的连续葡萄糖监测时间序列

Louis A Gomez¹, Adedolapo Aishat Toye¹, R Stanley Hum²

¹Stevens Institute of Technology, Hoboken, NJ, USA.

Journal of diabetes science and technology

|June 23, 2023

概括

研究人员开发了一种新方法来改进模拟血糖 (BG) 数据,用于1型糖尿病研究. 这种方法通过结合现实的数据缺失和错误来增强BG预测算法测试,弥合模拟和现实世界性能之间的差距.

科学领域:

生物医学工程生物医学工程
数据科学数据科学数据科学
糖尿病技术技术

背景情况:

模拟数据对于对比血糖 (BG) 预测和控制算法至关重要.
专家创建的模型和黑子方法,如GANs提供有限的现实主义和诊断能力,用于现实世界的性能.
现有的模拟方法缺乏真实连续血糖监测 (CGM) 数据的复杂特征,阻碍了准确的算法评估.

研究的目的:

开发一种新的方法来增强模拟的BG数据,并从真实的CGM数据中获得现实的缺失和错误属性.
提高模拟CGM数据的准确性,以便对BG预测算法进行更严格的测试和基准测试.
为了减少在模拟数据和真实CGM数据上测试的算法之间的性能差距.

主要方法:

从真实CGM数据集中学习缺失和错误特征 (OpenAPS,OhioT1DM,RCT,种族差异).
增强模拟BG数据与这些学习的属性来模仿现实世界的数据挑战.
使用增强模拟数据对比标准模拟实践 (随机丢失,高斯噪声,CGM错误模型) 评估了BG预测性能.

主要成果:

拟议的方法证明了与真实数据相比的最小性能差异,与随机丢失和高斯噪声相比,对于缺失的数据和单独的错误效应.
结合方法在大多数数据集中显著优于高斯噪声和随机丢失,但俄俄T1DM除外.
开发的错误模型显著提高了各种数据集的结果,表明模拟现实性得到了改进.

结论:

在基于模拟数据和真实数据的BG预测之间存在显著的绩效差距.
拟议的方法有效地弥补了这一差距,使得性能估计更为现实.
研究人员现在可以严格测试算法,并获得可靠的现实世界的性能见解,而无需过度装配或广泛的数据收集.

关键词:

预测血糖的预测不断监测葡萄糖的情况.数据增强数据增强这是一个错误的错误错误.缺失的数据缺失的数据模拟模拟是指一个模拟模拟.

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