Efficient Screening in Obstructive Sleep Apnea Using Sequential Machine Learning Models, Questionnaires, and Pulse Oximetry Signals: Mixed Methods Study
Although a composite model using multiple saturation parameters exhibits higher accuracy, optimizing this model by identifying key factors is essential. Both models demonstrated adequate at-home screening capabilities for sleep disorders, particularly for patients unsuitable for in-laboratory sleep studies.
Authors
- Hsin-Jung Tsai (ORCID: https://orcid.org/0000-0002-1387-9240)
- Shih‐Jen Tsai (ORCID: https://orcid.org/0000-0002-9987-022X)
- Albert C. Yang (ORCID: https://orcid.org/0000-0003-2794-9649)
- Nai-Yu Kuo
Institutions
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-12-19
- DOI
- https://doi.org/10.2196/51615
- Citations
- 5
- Primary Topic
- Obstructive Sleep Apnea Research
- Type
- article
- Field-Weighted Citation Impact
- 1.63
Funders
- University of Minnesota
- University of Washington
- Johns Hopkins University
- Case Western Reserve University
- York University
- Shanghai Educational Development Foundation
- National Science and Technology Council
- University of California, Davis
- National Heart, Lung, and Blood Institute