Improving short-term prandial blood glucose outcomes for people with type 1 diabetes, a complex disease that affects nearly 10 million people worldwide. We aim to leverage semi-supervised learning to identify unlabelled meals in time-series blood glucose data, develop meal-scoring functions, and explore causal machine-learning techniques.
Additional features were added to my data set creation function. It now incorporates an auto-labeler (incomplete) meal roll-up for better labeling of change points.
Additional features were added to my data set creation function. It now incorporates an auto-labeler (incomplete) meal roll-up for better labeling of change points.