Chronobiologically-Informed Features from CGM Data Provide Unique Information for XGBoost Prediction of Longer-Term Glycemic Dysregulation in 8,000 Individuals with Type-2 Diabetes
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This collection contains the tabulated data used to perform the machine learning components of the manuscript Chronobiologically-Informed Features from CGM Data Provide Unique Information for XGBoost Prediction of Longer-Term Glycemic Dysregulation in 8,000 Individuals with Type-2 Diabetes.
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UC San Diego Library Digital Collections
创建时间：
2025-02-25
