Hameed Hadia, Kleinberg Samantha
Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USA.
Computer Science, Stevens Institute of Technology, Hoboken, NJ, USA.
Proc Mach Learn Res. 2020 Aug;126:871-894.
Managing a chronic disease like Type 1 diabetes (T1D) is both challenging and time consuming, but new technologies that allow continuous measurement of glucose and delivery of insulin have led to significant improvements. The development of an artificial pancreas (AP), which algorithmically determines insulin dosing and delivers insulin in a fully automated way, may transform T1D care but it is not yet widely available. Patient-led alternatives, like the Open Artificial Pancreas (OpenAPS), are being used by hundreds of individuals and have also led to a dramatic increase in the availability of patient generated health data (PGHD). All APs require an accurate forecast of blood glucose (BG). While there have been efforts to develop better forecasts and apply new ML techniques like deep learning to this problem, methods are often tested on small controlled datasets that do not indicate how they may perform in reality - and the most advanced methods have not always outperformed the simplest. We introduce a rigorous comparison of BG forecasting using both a small controlled research dataset and large heterogeneous PGHD. Our comparison advances the state of the art in BG forecasting by providing insight into how methods may fare when moving beyond small controlled studies to real-world use.
管理像1型糖尿病（T1D）这样的慢性病既具有挑战性又耗时，但允许持续测量血糖和输送胰岛素的新技术已带来显著改善。人工胰腺（AP）的开发，即通过算法确定胰岛素剂量并以全自动方式输送胰岛素，可能会改变T1D的护理方式，但目前尚未广泛应用。由患者主导的替代方案，如开放式人工胰腺（OpenAPS），正被数百人使用，也导致了患者生成健康数据（PGHD）的可用性大幅增加。所有人工胰腺都需要准确预测血糖（BG）。虽然已经努力开发更好的预测方法并将深度学习等新的机器学习技术应用于这个问题，但方法通常在小型受控数据集上进行测试，这些数据集无法表明它们在实际中的表现如何——而且最先进的方法并不总是优于最简单的方法。我们使用小型受控研究数据集和大型异质PGHD对血糖预测进行了严格比较。我们的比较通过深入了解方法在从小型受控研究转向实际应用时的表现，推动了血糖预测领域的技术水平。
