Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment.
- DOI
- 10.3390/diagnostics16162606
- Published
- 2026 Aug 17
- Container
- Diagnostics (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/diagnostics16162606,
title = {Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment.},
author = {Kabak M and Irmak H and Kılıç AR and Çil B},
year = {2026},
journal = {Diagnostics (Basel, Switzerland)},
doi = {10.3390/diagnostics16162606},
url = {https://doi.org/10.3390/diagnostics16162606}
}RIS
TY - JOUR TI - Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment. AU - Kabak M AU - Irmak H AU - Kılıç AR AU - Çil B PY - 2026 JO - Diagnostics (Basel, Switzerland) DO - 10.3390/diagnostics16162606 UR - https://doi.org/10.3390/diagnostics16162606 ER -
APA
M, K., H, I., AR, K., & B, Ç. (2026). Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment.. Diagnostics (Basel, Switzerland). https://doi.org/10.3390/diagnostics16162606
Source records
- pubmed · retrieved 2026-09-24T21:55:27.976Z