Quantifying glucose and glucose variability from CGM devices
# cgmquantify: python package for analyzing glucose and glucose variability [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified.
Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data.
#### [User Guide](https://github.com/brinnaebent/cgmquantify/wiki/User-Guide) #### [Issue Tracking](https://github.com/brinnaebent/cgmquantify/issues)
#### Installation: * Recommended: pip install cgmanalysis * git clone [repo](https://github.com/brinnaebent/cgmquantify.git)
#### Dependencies: (these will be downloaded upon installation with pip) pandas, numpy, matplotlib, statsmodels, datetime
>Coming soon - >* Currently only supports Dexcom CGM, more CGM coming soon >* Integration with food logs, myFitnessPal food logs >* Machine Learning methods for discovering trends in CGM data
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size cgmquantify-0.3.zip (6.1 kB)||File type Source||Python version None||Upload date||Hashes View|