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.
- DOI
- 10.1371/journal.pdig.0000815
- Published
- 2025 Apr
- Container
- PLOS digital health
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pdig.0000815,
title = {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.},
author = {Burks JH and Joe L and Kanjaria K and Monsivais C and O'laughlin K and Smarr BL},
year = {2025},
journal = {PLOS digital health},
doi = {10.1371/journal.pdig.0000815},
url = {https://doi.org/10.1371/journal.pdig.0000815}
}RIS
TY - JOUR TI - 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. AU - Burks JH AU - Joe L AU - Kanjaria K AU - Monsivais C AU - O'laughlin K AU - Smarr BL PY - 2025 JO - PLOS digital health DO - 10.1371/journal.pdig.0000815 UR - https://doi.org/10.1371/journal.pdig.0000815 ER -
APA
JH, B., L, J., K, K., C, M., K, O., & BL, S. (2025). 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.. PLOS digital health. https://doi.org/10.1371/journal.pdig.0000815
Source records
- pubmed · retrieved 2026-09-26T07:06:36.948Z