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Rights Holder and Contact
O'Laughlin, Kate B.; Monsivias, Carlos S.; Joe, Leslie R.; Kanjaria, Karina J.
Description
Completed as a Capstone project for the Data Science & Engineering MAS program (DSE260 Capstone), this project utilizes Dexcom's data from their Continuous Glucose Monitors (CGM) to predict and describe Type 2 diabetic patients' future time above or below healthy glucose levels. The time series data, recorded in 5 minute intervals over 365 days for 8000 patients, is used to extract features such as entropy (predictability), variance (from Poincaré plots), among others. These features—alongside demographic data such as treatment type, patient age, and patient sex—are used in XGBoost classification models to predict if the amount of time a patient will spend out of range the next day is more, less, or the same as the amount of time a patient spent out of range the day before. The XGBoost classification models also provide feature importance, which informs the patient of which features affect their outcome the most. This project used pre-existing data from Dexcom. However, this data is not available for sharing due to Dexcom's licensing terms. Also, we did not receive permission to share the Dexcom model. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) O’Laughlin, Kate B.; Monsivias, Carlos S.; Joe, Leslie R.; Kanjaria, Karina J.; Burks, Jamie H.; Smarr, Benjamin L. (2024). Long Term Horizon Predictions and Feature Explainability of Time Series Continuous Glucose Monitor Data. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0XW4K0B
Type
dataset
Identifier
ark:/20775/bb9329453v
Language
English
Subject
Continuous glucose monitoring Time series Data science Machine learning Capstone projects XGBoost Dexcom Feature extraction PySpark Type 2 Diabetes Mellitus Data Science & Engineering Master of Advanced Study (DSE MAS) DSE MAS - 2023 Cohort
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