Explainable Machine Learning Assists in Revealing Associations Between Polysomnographic Biomarkers and Incident Type 2 Diabetes in Men.

Nguyen DP, Catcheside P, Lechat B, Wittert G, Vakulin A, Adams R, Appleton SL.

DOI: 10.2147/nss.s512262
Published: 2025-08-30
Container: Nat Sci Sleep
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.2147/nss.s512262,
  title = {Explainable Machine Learning Assists in Revealing Associations Between Polysomnographic Biomarkers and Incident Type 2 Diabetes in Men.},
  author = {Nguyen DP and  Catcheside P and  Lechat B and  Wittert G and  Vakulin A and  Adams R and  Appleton SL.},
  year = {2025},
  journal = {Nat Sci Sleep},
  doi = {10.2147/nss.s512262},
  url = {https://doi.org/10.2147/nss.s512262}
}

RIS

TY  - JOUR
TI  - Explainable Machine Learning Assists in Revealing Associations Between Polysomnographic Biomarkers and Incident Type 2 Diabetes in Men.
AU  - Nguyen DP
AU  -  Catcheside P
AU  -  Lechat B
AU  -  Wittert G
AU  -  Vakulin A
AU  -  Adams R
AU  -  Appleton SL.
PY  - 2025
JO  - Nat Sci Sleep
DO  - 10.2147/nss.s512262
UR  - https://doi.org/10.2147/nss.s512262
ER  -

APA

DP, N., P, C., B, L., G, W., A, V., R, A., & SL., A. (2025). Explainable Machine Learning Assists in Revealing Associations Between Polysomnographic Biomarkers and Incident Type 2 Diabetes in Men.. Nat Sci Sleep. https://doi.org/10.2147/nss.s512262

Source records

europe-pmc · retrieved 2026-09-25T07:58:26.882Z