insulearner 0.1.6

Estimate Insulin Pump Settings using Machine Learning

InsuLearner: Estimating Insulin Pump Settings with Machine Learning

Author: Cameron Summers

Author's Website: www.CameronSummers.com

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Warning:

This code can have significant impact on insulin dosing. There are no guardrails included here so it's possible to get poor results in some circumstances. Check with your doctor before making any changes to dosing settings.

Overview:

This is the code underlying my article where I describe in plain language the machine learning approach I developed to compute the insulin pump settings for my son.

This library estimates personalized insulin pump settings from historical insulin and carbohydrate information. Currently, it interfaces with Tidepool accounts to retrieve data.

The code uses machine learning to estimate:

• Carbohydate Ratio (CIR)
• Basal Rate
• Insulin Sensivity Factor (ISF)

Historical carbohydrate and insulin data directly estimate CIR and Basal Rate using Linear Regression. An estimate of Carbohydrate Sensitivity Factor (CSF) and the estimate CIR is used to estimate ISF.

Here is an example plot of a fitted model and estimated settings:

Dependencies

Developed and tested with Python 3.9

Installation

For easy CLI tool create a virtual environment and then run:

pip install insulearner

For digging into the code to understand what's going on (recommended):

git clone https://github.com/scaubrey/InsuLearner

Usage (CLI)

The install with pip provides a command line interface so you can run insulearner.

CLI Examples

If you don't know your Carbohydrate Sensitivity Factor (CSF) I devised an estimator based on height and weight. Note: this estimator is an educated guess and not validated. It's probably better if you estimate it yourself.

insulearner <your_tidepool_email> <your_tidepool_password> --num_days 60 --height_inches 72 --weight_lbs 200 --gender male

If you do have an estimate of your CSF through your own testing:

insulearner <your_tidepool_email> <your_tidepool_password> --num_days 60 --CSF 4.2

More CLI Options

--agg_period_window_size_hours This is the size of the time period in days over which aggregate insulin and carb data. I theorized in my article that longer periods would converge to better estimates of CIR and Basal Rate. Default is 1 day (24 hours) but I have seen good results with many more days.

--agg_period_hop_size_hours This is how much to shift over the aggregation window for each data point. For example, if the window size above is 72 hrs and this is 24 hours, then the aggregation for the first data point will be days 1-3, the second data point will be days 2-4, and the third data point will be days 3-5, and so on.

--estimate_agg_boundaries If set this will use an autocorrelation-like algorithm to estimate the hour of the day when blood glucose movement is the least active, ie isolating associated insulin and carb effects.

Usage (code)

Once installed with pip, you can use the functions in your code:

>>> from InsuLearner.insulearner import analyze_settings_lr

Algorithms

There are three algorithms I developed that are in this code and worth being aware of:

1. An autocorrelation-like algorithm to find inactive periods in data in order to help denoise the data for fitting a model.
2. Linear Regression to estimate Carb Ratio (CIR), Basal Rate, and Insulin Sensitivity Factor (ISF)
   1. This is methodology is described in my article.
3. Estimating Carb Sensitivity Factor (CSF)
   1. CSF is used to derive ISF from the estimated CIR from the Linear Regression. It's best to estimate CSF through testing, but I've provided an algorithm to approximate it based on blood volume computed via height and weight. While based on knowledge of biology this algorithm has not been validated.

Tests

I included a couple of basic regression tests in the tests folder of the code that can be run with pytest.

Acknowledgements

Special thanks to Tidepool for doing great things for the diabetes community.

How to Contribute

Contribute Code:

Hello! Thanks for considering contributing.

• Step 1. Please open an Issue with a description of what you're trying to add/fix/change
• Step 2. Fork and create a feature branch in the format /
• Step 3. Please squash all your commits into one with a good commit message before opening a pull request
• Step 4. Open a pull request, reference your original issue, and provide a concise description of how your changes fixed the issue
• Step 5. Your PR requires approval before it can be merged.

Contribute Results:

If you've run this and had success or issues, please consider sharing so it can be improved. You can message me on my website.

License: GPLv3