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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| 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. |
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| Authors: | Burks JH; Shiu Chen - Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America., Joe L; Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California, United States of America., Kanjaria K; Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California, United States of America., Monsivais C; Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California, United States of America., O'laughlin K; Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California, United States of America., Smarr BL; Shiu Chen - Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America.; Halicioğlu Data Science Institute, University of California San Diego, La Jolla, California, United States of America. |
| Source: | PLOS digital health [PLOS Digit Health] 2025 Apr 09; Vol. 4 (4), pp. e0000815. Date of Electronic Publication: 2025 Apr 09 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 2767-3170 |
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| DOI: | 10.1371/journal.pdig.0000815 |
