Machine Learning Identification of Obstructive Sleep Apnea Severity through the Patient Clinical Features: A Retrospective Study

Objectives: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild...

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Detalles Bibliográficos
Autores principales: Maniaci, Antonino, Riela, Paolo Marco, Iannella, Giannicola, Lechien, Jerome Rene, La Mantia, Ignazio, De Vincentiis, Marco, Cammaroto, Giovanni, Calvo-Henriquez, Christian, Di Luca, Milena, Chiesa Estomba, Carlos, Saibene, Alberto Maria, Pollicina, Isabella, Stilo, Giovanna, Di Mauro, Paola, Cannavicci, Angelo, Lugo, Rodolfo, Magliulo, Giuseppe, Greco, Antonio, Pace, Annalisa, Meccariello, Giuseppe, Cocuzza, Salvatore, Vicini, Claudio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056063/
https://www.ncbi.nlm.nih.gov/pubmed/36983857
http://dx.doi.org/10.3390/life13030702

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056063/
https://www.ncbi.nlm.nih.gov/pubmed/36983857
http://dx.doi.org/10.3390/life13030702

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