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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

438 related items for PubMed ID: 28590499

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  • 3. Application of machine learning to predict obstructive sleep apnea syndrome severity.
    Mencar C, Gallo C, Mantero M, Tarsia P, Carpagnano GE, Foschino Barbaro MP, Lacedonia D.
    Health Informatics J; 2020 Mar; 26(1):298-317. PubMed ID: 30696334
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  • 4. Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature.
    Ye P, Qin H, Zhan X, Wang Z, Liu C, Song B, Kong Y, Jia X, Qi Y, Ji J, Chang L, Ni X, Tai J.
    Am J Otolaryngol; 2023 Mar; 44(2):103714. PubMed ID: 36738700
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  • 5. The Prioritization of Clinical Risk Factors of Obstructive Sleep Apnea Severity Using Fuzzy Analytic Hierarchy Process.
    Maranate T, Pongpullponsak A, Ruttanaumpawan P.
    Comput Math Methods Med; 2015 Mar; 2015():257856. PubMed ID: 26221183
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  • 6. Exploiting temporal and nonstationary features in breathing sound analysis for multiple obstructive sleep apnea severity classification.
    Kim J, Kim T, Lee D, Kim JW, Lee K.
    Biomed Eng Online; 2017 Jan 07; 16(1):6. PubMed ID: 28086902
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  • 7. A Novel Model to Estimate Key Obstructive Sleep Apnea Endotypes from Standard Polysomnography and Clinical Data and Their Contribution to Obstructive Sleep Apnea Severity.
    Dutta R, Delaney G, Toson B, Jordan AS, White DP, Wellman A, Eckert DJ.
    Ann Am Thorac Soc; 2021 Apr 07; 18(4):656-667. PubMed ID: 33064953
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  • 8. Validation of overnight oximetry to diagnose patients with moderate to severe obstructive sleep apnea.
    Hang LW, Wang HL, Chen JH, Hsu JC, Lin HH, Chung WS, Chen YF.
    BMC Pulm Med; 2015 Mar 20; 15():24. PubMed ID: 25880649
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  • 9. Reviewing the connection between speech and obstructive sleep apnea.
    Espinoza-Cuadros F, Fernández-Pozo R, Toledano DT, Alcázar-Ramírez JD, López-Gonzalo E, Hernández-Gómez LA.
    Biomed Eng Online; 2016 Feb 20; 15():20. PubMed ID: 26897500
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  • 10. 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 19; 23(1):230. PubMed ID: 37858225
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  • 11. 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; 15(11):1599-1608. PubMed ID: 31739849
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  • 17. Evaluation of a 2-channel portable device and a predictive model to screen for obstructive sleep apnea in a laboratory environment.
    Zou J, Meng L, Liu Y, Xu X, Liu S, Guan J, Yin S, Yi H.
    Respir Care; 2015 Mar 15; 60(3):356-62. PubMed ID: 25406349
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  • 18. A new simple efficient classification technique for severity of sleep apnea with mathematical model and interpretation.
    Hossen A.
    Technol Health Care; 2019 Mar 15; 27(4):389-406. PubMed ID: 30829627
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  • 19. Does the severity of obstructive sleep apnea predict patients requiring high continuous positive airway pressure?
    Oksenberg A, Arons E, Froom P.
    Laryngoscope; 2006 Jun 15; 116(6):951-5. PubMed ID: 16735905
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  • 20. Rapid risk stratification for obstructive sleep apnea, based on snoring severity and body mass index.
    Morris LG, Kleinberger A, Lee KC, Liberatore LA, Burschtin O.
    Otolaryngol Head Neck Surg; 2008 Nov 15; 139(5):615-8. PubMed ID: 18984252
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