Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data

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.