Machine learning analysis of continuous glucose monitoring identifies a novel dysglycemic phenotype found in most people with cystic fibrosis.

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Bibliographic Details
Title: Machine learning analysis of continuous glucose monitoring identifies a novel dysglycemic phenotype found in most people with cystic fibrosis.
Authors: Song J; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA. Electronic address: stuart.song@duke.edu., Alvarez J; Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, USA., Gent A; Department of Surgery, Duke University School of Medicine, Durham, North Carolina, USA; Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA., Gillespie S; Pediatric Biostatistics Core, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA., Harris RA; Georgia Prevention Institute, Medical College of Georgia, Augusta University, Augusta, Georgia, USA., McNeany J; Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA., Daley T; Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA., Kamaleswaran R; Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA; Department of Surgery, Duke University School of Medicine, Durham, North Carolina, USA; Department of Anesthesiology, Duke University School of Medicine, Durham, North Carolina, USA., Stecenko A; Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA.
Source: Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society [J Cyst Fibros] 2026 May; Vol. 25 (3), pp. 474-481. Date of Electronic Publication: 2026 Apr 23.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101128966 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-5010 (Electronic) Linking ISSN: 15691993 NLM ISO Abbreviation: J Cyst Fibros Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-5010
DOI:10.1016/j.jcf.2026.04.001