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

Clinical Information and Decision Making (2004) Research Instruments, Questionnaires, and Tools (764) Insomnia and Sleep Hygiene (258) Machine Learning (2167) New Methods (1153) Sleep Apnea (38) Sleep Monitoring, Sleep Quality, Sleep Disorders (281) mHealth for Diagnosis (82)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51615, first published .
Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study

Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study

Authors of this article:

Nai-Yu Kuo1, 2, 3 Author Orcid Image ;   Hsin-Jung Tsai2 Author Orcid Image ;   Shih-Jen Tsai2, 3 Author Orcid Image ;   Albert C Yang1, 2, 4, 5 Author Orcid Image
Article Authors Cited by (2) Tweetations (1) Metrics

Journals

  1. Wang Y, Liu Q, Min F, Wang H. PSG-MAE: Robust Multitask Sleep Event Monitoring Using Multichannel PSG Reconstruction and Inter-Channel Contrastive Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2026;34:274 View
  2. Chi H, Dabbs-Brown A, Jurek-Loughrey A, Mulhall J, Pham T, Doan N, Tran V, Zhang Z, Nguyen X, An Y, Li P, Nguyen P, Hoang T, Shi X, Vandierendonck H, Bailly S, Pépin J, Mai T. Obstructive Sleep Apnea Prediction: A Comprehensive Review and Comparative Study. Machine Learning 2026;115(2) View