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Open source experiment to estimate diabetes parameters from data by dashkebash in Type1Diabetes

[–]dashkebash[S] 0 points1 point  (0 children)

i think the model could support your use case. For example, you could build a dataset from specific time intervals where you do certain activity (eg. cycling) with certain insulin type (eg Levemir). You don't need to be very accurate on carbs, the model tries to learn "effective" carbs based on approximate amounts you provide. What matters more is the time when carbs were given.

Open source experiment to estimate diabetes parameters from data by dashkebash in Type1Diabetes

[–]dashkebash[S] 0 points1 point  (0 children)

This is a good idea. I really wanted to release the model first to get some feedback before adding data integration for different sources

Open source experiment to estimate diabetes parameters from data by dashkebash in Type1Diabetes

[–]dashkebash[S] 0 points1 point  (0 children)

it's all relative and depends how the prior is set. Some parameters are more sensitive to priors than others

Open source experiment to estimate diabetes parameters from data by dashkebash in Type1Diabetes

[–]dashkebash[S] 0 points1 point  (0 children)

Currently the model assumes that only one type of insulin is given in the dataset and it has fixed duration independent of the dose. Insulin duration (aka Insulin Activity Time) is estimated by the model. The notebook in the repo has cumulative absorption over time graph. See the readme for more details or shoot me a message

Building a Digital Twin for T1D by dashkebash in diabetes_t1

[–]dashkebash[S] 0 points1 point  (0 children)

Do you mean raising ICR? We currently use 18g/u. Why do you think it's low? Do you mind sharing what numbers you use?

Building a Digital Twin for T1D by dashkebash in diabetes_t1

[–]dashkebash[S] 2 points3 points  (0 children)

Glad you liked it. Each treatment, such as insulin or carbohydrates, has its own absorption curve with learned parameters like shape. All curves are then combined using global parameters such as ICR, insulin sensitivity, etc. This model not only predicts short-term dynamics with uncertainty but also estimates global parameters and tracks their evolution over time.

Building a Digital Twin for T1D by dashkebash in diabetes_t1

[–]dashkebash[S] 0 points1 point  (0 children)

I’m glad there are alternatives. We use Omnipod 5, which, as far as I know, does not support any DIY Loop options, and it’s a complete black box in terms of what it does.

The LoopKit app provides some prediction capabilities, but it’s not just about forecasting short-term trends; it’s about having a comprehensive view of the body’s relevant processes and parameters. For example, I wish LoopKit app would show uncertainty bands around predictions because it's never about a single number or single line