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...
Main Authors: | Antonino Maniaci, Paolo Marco Riela, Giannicola Iannella, Jerome Rene Lechien, Ignazio La Mantia, Marco De Vincentiis, Giovanni Cammaroto, Christian Calvo-Henriquez, Milena Di Luca, Carlos Chiesa Estomba, Alberto Maria Saibene, Isabella Pollicina, Giovanna Stilo, Paola Di Mauro, Angelo Cannavicci, Rodolfo Lugo, Giuseppe Magliulo, Antonio Greco, Annalisa Pace, Giuseppe Meccariello, Salvatore Cocuzza, Claudio Vicini |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Life |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-1729/13/3/702 |
Similar Items
-
Neurocognitive Performance Improvement after Obstructive Sleep Apnea Treatment: State of the Art
by: Isabella Pollicina, et al.
Published: (2021-12-01) -
The Effects of Barbed Repositioning Pharyngoplasty in Positional and Non-Positional OSA Patients: A Retrospective Analysis
by: Giovanni Cammaroto, et al.
Published: (2022-11-01) -
Role of sleep questionnaires in predicting obstructive sleep apnea amongst interstitial lung diseases patients
by: Tome Kamgo, et al.
Published: (2023-01-01) -
Diagnosis of Obstructive Sleep Apnea in Patients with Allergic and Non-Allergic Rhinitis
by: Annalisa Pace, et al.
Published: (2020-09-01) -
Clinical Application of Pediatric Sleep Endoscopy: An International Survey
by: Giannicola Iannella, et al.
Published: (2024-01-01)