Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches
Máximo Domínguez-Guerrero, Daniel Álvarez, Verónica Barroso-García, María Fernández-Vaquerizo, Tomás Ruiz-Albi, Roberto Hornero · 2026
Read Early prediction of longitudinal treatment adherence in obstructive sleep apnea using machine learning approaches by Máximo Domínguez-Guerrero, Daniel Álvarez, Verónica Barroso-García, María Fernández-Vaquerizo, Tomás Ruiz-Albi, Roberto Hornero (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13202768.txt.
Description
BackgroundContinuous Positive Airway Pressure (CPAP) is the most prescribed treatment for Obstructive Sleep Apnea (OSA), but adherence remains a critical challenge, especially long term. This study aims to predict CPAP adherence at 3, 6, and 12 months based on baseline patient status and usage data from initial 30 days of treatment.ResultsA retrospective cohort of 2180 patients was analyzed.
Catalog record
| CREATOR | Máximo Domínguez-Guerrero, Daniel Álvarez, Verónica Barroso-García, María Fernández-Vaquerizo, Tomás Ruiz-Albi, Roberto Hornero |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |