Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance

Baiying Lu, Biratal Wagle, Zhaohui Liang, Yanjun Cui, Temiloluwa Prioleau

Open source

DOI: 10.1371/journal.pdig.0001633
Published: 2026-09-03
Container: PLOS Digital Health
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pdig.0001633,
  title = {Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance},
  author = {Baiying Lu and Biratal Wagle and Zhaohui Liang and Yanjun Cui and Temiloluwa Prioleau},
  year = {2026},
  journal = {PLOS Digital Health},
  doi = {10.1371/journal.pdig.0001633},
  url = {https://doi.org/10.1371/journal.pdig.0001633}
}

RIS

TY  - JOUR
TI  - Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance
AU  - Baiying Lu
AU  - Biratal Wagle
AU  - Zhaohui Liang
AU  - Yanjun Cui
AU  - Temiloluwa Prioleau
PY  - 2026
JO  - PLOS Digital Health
DO  - 10.1371/journal.pdig.0001633
UR  - https://doi.org/10.1371/journal.pdig.0001633
ER  -

APA

Lu, B., Wagle, B., Liang, Z., Cui, Y., & Prioleau, T. (2026). Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance. PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0001633

Source records

crossref · retrieved 2026-09-26T03:19:15.441Z