314 related articles for article (PubMed ID: 34717928)
1. Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples.
Holfinger SJ; Lyons MM; Keenan BT; Mazzotti DR; Mindel J; Maislin G; Cistulli PA; Sutherland K; McArdle N; Singh B; Chen NH; Gislason T; Penzel T; Han F; Li QY; Schwab R; Pack AI; Magalang UJ
Chest; 2022 Mar; 161(3):807-817. PubMed ID: 34717928
[TBL] [Abstract][Full Text] [Related]
2. Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index.
Kuan YC; Hong CT; Chen PC; Liu WT; Chung CC
Math Biosci Eng; 2022 Aug; 19(11):11409-11421. PubMed ID: 36124597
[TBL] [Abstract][Full Text] [Related]
3. Combined nocturnal pulse oximetry and questionnaire-based obstructive sleep apnea screening - A cohort study.
Mashaqi S; Staebler D; Mehra R
Sleep Med; 2020 Aug; 72():157-163. PubMed ID: 32653766
[TBL] [Abstract][Full Text] [Related]
4. Use and Performance of the STOP-Bang Questionnaire for Obstructive Sleep Apnea Screening Across Geographic Regions: A Systematic Review and Meta-Analysis.
Pivetta B; Chen L; Nagappa M; Saripella A; Waseem R; Englesakis M; Chung F
JAMA Netw Open; 2021 Mar; 4(3):e211009. PubMed ID: 33683333
[TBL] [Abstract][Full Text] [Related]
5. Preoperative screening for obstructive sleep apnea in cardiovascular patients - How useful is STOP-BANG questionnaire in the Indian context?
Sreedharan SE; Mitta N; Unnikrishnan KP; Paul R; Pillai V
Ann Card Anaesth; 2021; 24(3):308-312. PubMed ID: 34269259
[TBL] [Abstract][Full Text] [Related]
6. Exploring the STOP-BANG questionnaire for obstructive sleep apnea screening in seniors.
Martins EF; Martinez D; Cortes AL; Nascimento N; Brendler J
J Clin Sleep Med; 2020 Feb; 16(2):199-206. PubMed ID: 31992408
[TBL] [Abstract][Full Text] [Related]
7. Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea Severity to Optimal Utilization of Polysomnography Resources?
Bozkurt S; Bostanci A; Turhan M
Methods Inf Med; 2017 Aug; 56(4):308-318. PubMed ID: 28590499
[TBL] [Abstract][Full Text] [Related]
8. Simplifying the Screening of Obstructive Sleep Apnea With a 2-Item Model, No-Apnea: A Cross-Sectional Study.
Duarte RLM; Rabahi MF; Magalhães-da-Silveira FJ; de Oliveira-E-Sá TS; Mello FCQ; Gozal D
J Clin Sleep Med; 2018 Jul; 14(7):1097-1107. PubMed ID: 29991419
[TBL] [Abstract][Full Text] [Related]
9. Self-helped detection of obstructive sleep apnea based on automated facial recognition and machine learning.
Chen Q; Liang Z; Wang Q; Ma C; Lei Y; Sanderson JE; Hu X; Lin W; Liu H; Xie F; Jiang H; Fang F
Sleep Breath; 2023 Dec; 27(6):2379-2388. PubMed ID: 37278870
[TBL] [Abstract][Full Text] [Related]
10. Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study.
Calvillo-Argüelles O; Sierra-Fernández CR; Padilla-Ibarra J; Rodriguez-Zanella H; Balderas-Muñoz K; Arias-Mendoza MA; Martínez-Sánchez C; Selmen-Chattaj S; Dominguez-Mendez BE; van der Harst P; Juarez-Orozco LE
Chest; 2020 Oct; 158(4):1669-1679. PubMed ID: 32343966
[TBL] [Abstract][Full Text] [Related]
11. Prediction Models for Obstructive Sleep Apnea in Korean Adults Using Machine Learning Techniques.
Kim YJ; Jeon JS; Cho SE; Kim KG; Kang SG
Diagnostics (Basel); 2021 Mar; 11(4):. PubMed ID: 33808100
[TBL] [Abstract][Full Text] [Related]
12. BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.
Huo J; Quan SF; Roveda J; Li A
Sleep Breath; 2023 May; 27(2):449-457. PubMed ID: 35482152
[TBL] [Abstract][Full Text] [Related]
13. Validation of a Portuguese version of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnea: Analysis in a sleep clinic.
Reis R; Teixeira F; Martins V; Sousa L; Batata L; Santos C; Moutinho J
Rev Port Pneumol (2006); 2015; 21(2):61-8. PubMed ID: 25926368
[TBL] [Abstract][Full Text] [Related]
14. Development and application of a machine learning-based predictive model for obstructive sleep apnea screening.
Liu K; Geng S; Shen P; Zhao L; Zhou P; Liu W
Front Big Data; 2024; 7():1353469. PubMed ID: 38817683
[TBL] [Abstract][Full Text] [Related]
15. Application and interpretation of machine learning models in predicting the risk of severe obstructive sleep apnea in adults.
Shi Y; Zhang Y; Cao Z; Ma L; Yuan Y; Niu X; Su Y; Xie Y; Chen X; Xing L; Hei X; Liu H; Wu S; Li W; Ren X
BMC Med Inform Decis Mak; 2023 Oct; 23(1):230. PubMed ID: 37858225
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of the Arabic version of STOP-Bang questionnaire as a screening tool for obstructive sleep apnea.
Alhouqani S; Al Manhali M; Al Essa A; Al-Houqani M
Sleep Breath; 2015 Dec; 19(4):1235-40. PubMed ID: 25758298
[TBL] [Abstract][Full Text] [Related]
17. Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.
Ferreira-Santos D; Amorim P; Silva Martins T; Monteiro-Soares M; Pereira Rodrigues P
J Med Internet Res; 2022 Sep; 24(9):e39452. PubMed ID: 36178720
[TBL] [Abstract][Full Text] [Related]
18. Predicting Nondiagnostic Home Sleep Apnea Tests Using Machine Learning.
Stretch R; Ryden A; Fung CH; Martires J; Liu S; Balasubramanian V; Saedi B; Hwang D; Martin JL; Della Penna N; Zeidler MR
J Clin Sleep Med; 2019 Nov; 15(11):1599-1608. PubMed ID: 31739849
[TBL] [Abstract][Full Text] [Related]
19. Comparative performance of screening instruments for obstructive sleep apnea in morbidly obese patients referred to a sleep laboratory: a prospective cross-sectional study.
Duarte RLM; Mello FCQ; Magalhães-da-Silveira FJ; Oliveira-E-Sá TS; Rabahi MF; Gozal D
Sleep Breath; 2019 Dec; 23(4):1123-1132. PubMed ID: 30734890
[TBL] [Abstract][Full Text] [Related]
20. Diagnostic performance of the STOP-Bang questionnaire as a screening tool for obstructive sleep apnea in different ethnic groups.
Waseem R; Chan MTV; Wang CY; Seet E; Tam S; Loo SY; Lam CKM; Hui DS; Chung F
J Clin Sleep Med; 2021 Mar; 17(3):521-532. PubMed ID: 33112227
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
