https://github.com/RobotPsychologist/bg_control
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.
https://github.com/RobotPsychologist/bg_control
changepoint-detection diabetes timeseries-forecasting
Last synced: 21 days ago
JSON representation
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.
- Host: GitHub
- URL: https://github.com/RobotPsychologist/bg_control
- Owner: RobotPsychologist
- Created: 2024-09-02T18:15:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-15T04:53:02.000Z (10 months ago)
- Last Synced: 2025-02-05T22:43:24.785Z (8 months ago)
- Topics: changepoint-detection, diabetes, timeseries-forecasting
- Language: Jupyter Notebook
- Homepage: https://blood-glucose-control.streamlit.app/
- Size: 324 MB
- Stars: 17
- Watchers: 2
- Forks: 42
- Open Issues: 38
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-causal-ai - bg_control - 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. *(Jupyter Notebook)* (🚀 GitHub Repositories / 🎓 Educational & Tutorial Resources)
- awesome-causal-ai - bg_control - 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. *(Jupyter Notebook)* (🚀 GitHub Repositories / 🎓 Educational & Tutorial Resources)