This paper is in the following e-collection/theme issue:

Digital Health Reviews (1186) Decision Support for Health Professionals (1137) Machine Learning (1384) Artificial Intelligence (1197) Clinical Information and Decision Making (1328)

Published on in Vol 24, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39452, first published .
Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review

Authors of this article:

Daniela Ferreira-Santos1, 2 Author Orcid Image ;   Pedro Amorim1, 2, 3 Author Orcid Image ;   Tiago Silva Martins2 Author Orcid Image ;   Matilde Monteiro-Soares1, 2, 4 Author Orcid Image ;   Pedro Pereira Rodrigues1, 2 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Journals

  1. Sharma M, Makwana P, Chad R, Acharya U. A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank. Applied Intelligence 2023;53(15):18681 View
  2. Casal-Guisande M, Ceide-Sandoval L, Mosteiro-Añón M, Torres-Durán M, Cerqueiro-Pequeño J, Bouza-Rodríguez J, Fernández-Villar A, Comesaña-Campos A. Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea. Diagnostics 2023;13(11):1854 View
  3. Ha S, Choi S, Lee S, Wijaya R, Kim J, Joo E, Kim J. Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study. Journal of Medical Internet Research 2023;25:e46520 View
  4. Liu Y, Xie S, Yang X, Chen J, Zhou J. Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children. Nature and Science of Sleep 2024;Volume 16:193 View
  5. BaHammam A. Artificial Intelligence in Sleep Medicine: The Dawn of a New Era. Nature and Science of Sleep 2024;Volume 16:445 View
  6. Yan L, Li Z, Li C, Chen J, Zhou X, Cui J, Liu P, Shen C, Chen C, Hong H, Xu G, Cui Z, Tian S. Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms. PLOS ONE 2024;19(5):e0303235 View
  7. Liu K, Geng S, Shen P, Zhao L, Zhou P, Liu W. Development and application of a machine learning-based predictive model for obstructive sleep apnea screening. Frontiers in Big Data 2024;7 View
  8. Pan Y, Zhao D, Zhang X, Yuan N, Yang L, Jia Y, Guo Y, Chen Z, Wang Z, Qu S, Bao J, Liu Y. Machine learning-Based model for prediction of Narcolepsy Type 1 in Patients with Obstructive Sleep Apnea with Excessive Daytime Sleepiness. Nature and Science of Sleep 2024;Volume 16:639 View

Books/Policy Documents

  1. El Sherbini A, Glicksberg B, Krittanawong C. Artificial Intelligence in Clinical Practice. View