IrinaStatsLab/GlucoBench

The official implementation of the paper "GlucoBench: Curated List of Continuous Glucose Monitoring Datasets with Prediction Benchmarks."

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This project helps diabetes researchers and medical professionals evaluate how well different methods can predict future glucose levels from continuous glucose monitoring (CGM) data. You input raw CGM readings and patient information, and it outputs performance benchmarks for various prediction models. This is for scientists, endocrinologists, or data analysts working on improving diabetes management.

No commits in the last 6 months.

Use this if you need a standardized way to compare and benchmark continuous glucose monitoring (CGM) prediction algorithms using a collection of real-world patient datasets.

Not ideal if you are looking for a complete, production-ready application to directly use for patient care or real-time glucose prediction.

diabetes-research glucose-monitoring predictive-modeling medical-data-analysis endocrinology
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 7 / 25
Maturity 8 / 25
Community 18 / 25

How are scores calculated?

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Jupyter Notebook

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Last pushed

Aug 20, 2024

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curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/IrinaStatsLab/GlucoBench"

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