Cargando…
Application of various machine learning techniques to predict obstructive sleep apnea syndrome severity
As the incidence of obstructive sleep apnea syndrome (OSAS) increases worldwide, the need for a new screening method that can compensate for the shortcomings of the traditional diagnostic method, polysomnography (PSG), is emerging. In this study, data from 4014 patients were used, and both supervise...
Autores principales: | Han, Hyewon, Oh, Junhyoung |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115886/ https://www.ncbi.nlm.nih.gov/pubmed/37076549 http://dx.doi.org/10.1038/s41598-023-33170-7 |
Ejemplares similares
-
Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults
por: Shi, Yewen, et al.
Publicado: (2023) -
Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques
por: Kim, Young Jae, et al.
Publicado: (2021) -
Obstructive sleep apnea in children with Down syndrome: is it possible to predict severe apnea?
por: Hizal, Mina, et al.
Publicado: (2021) -
Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea
por: Aiyer, Ishan, et al.
Publicado: (2022) -
Predictive performances of 6 data mining techniques for obstructive sleep apnea-hypopnea syndrome
por: Luo, Miao, et al.
Publicado: (2022)