Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for th...

Descripción completa

Detalles Bibliográficos
Autores principales: Tsai, Cheng-Yu, Huang, Huei-Tyng, Cheng, Hsueh-Chien, Wang, Jieni, Duh, Ping-Jung, Hsu, Wen-Hua, Stettler, Marc, Kuan, Yi-Chun, Lin, Yin-Tzu, Hsu, Chia-Rung, Lee, Kang-Yun, Kang, Jiunn-Horng, Wu, Dean, Lee, Hsin-Chien, Wu, Cheng-Jung, Majumdar, Arnab, Liu, Wen-Te
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694257/
https://www.ncbi.nlm.nih.gov/pubmed/36433227
http://dx.doi.org/10.3390/s22228630

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694257/
https://www.ncbi.nlm.nih.gov/pubmed/36433227
http://dx.doi.org/10.3390/s22228630

Ejemplares similares