I spent a long time trying to build predictive models that use complete and accurate carbs/protein/fat macronutrient data as well as insulin delivery. I found that there were enough non-linearities and other unmodelable factors at work in the BG data that led to very poor predictive ability. Considering protein & fat had a nil-to-negative effect on the models.
It's possible that accounting for heart rate as well as carbs & insulin might have better results, with enough data.
Yeah I was using the macro-nutrient approach as well, but then switched to simply providing GI (glycemic index) alongside the carbs in grams and found out that there was practically no difference between the two.
During training I artificially wobble the carb amounts so they are more like guesstimates than true carbs. This also partially fixed the quantile bands (before that they were too narrow).
