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.
We will also be running experiments on the simglucose simulated data. We'll need to understand how closely we can structure this data to resemble real world data. It would be good to have more data to work with when we get this working.
We will also be running experiments on the simglucose simulated data. We'll need to understand how closely we can structure this data to resemble real world data. It would be good to have more data to work with when we get this working.
https://github.com/jxx123/
The results of our implementation would be calling a function with our specifications that can essentially create all of the csvs in this directory:
https://github.com/jxx123/simglucose/tree/master/examples/results/2017-12-31_17-46-32