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Journal Abstract Search

213 related items for PubMed ID: 36433227

  • 1. Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.
    Tsai CY, Huang HT, Cheng HC, Wang J, Duh PJ, Hsu WH, Stettler M, Kuan YC, Lin YT, Hsu CR, Lee KY, Kang JH, Wu D, Lee HC, Wu CJ, Majumdar A, Liu WT.
    Sensors (Basel); 2022 Nov 09; 22(22):. PubMed ID: 36433227
    [Abstract] [Full Text] [Related]

  • 2. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events.
    Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC.
    Digit Health; 2023 Nov 09; 9():20552076231152751. PubMed ID: 36896329
    [Abstract] [Full Text] [Related]

  • 3. Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile.
    Tsai CY, Liu WT, Lin YT, Lin SY, Houghton R, Hsu WH, Wu D, Lee HC, Wu CJ, Li LYJ, Hsu SM, Lo CC, Lo K, Chen YR, Lin FC, Majumdar A.
    Inform Health Soc Care; 2022 Oct 02; 47(4):373-388. PubMed ID: 34886766
    [Abstract] [Full Text] [Related]

  • 4. Enhanced machine learning approaches for OSA patient screening: model development and validation study.
    Dai R, Yang K, Zhuang J, Yao L, Hu Y, Chen Q, Zheng H, Zhu X, Ke J, Zeng Y, Fan C, Chen X, Fan J, Zhang Y.
    Sci Rep; 2024 Aug 26; 14(1):19756. PubMed ID: 39187569
    [Abstract] [Full Text] [Related]

  • 5. Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features.
    Tsai CY, Kuan YC, Hsu WH, Lin YT, Hsu CR, Lo K, Hsu WH, Majumdar A, Liu YS, Hsu SM, Ho SC, Cheng WH, Lin SY, Lee KY, Wu D, Lee HC, Wu CJ, Liu WT.
    Diagnostics (Basel); 2021 Dec 27; 12(1):. PubMed ID: 35054218
    [Abstract] [Full Text] [Related]

  • 6. 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 Dec 27; 44(2):103714. PubMed ID: 36738700
    [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 11; 56(4):308-318. PubMed ID: 28590499
    [Abstract] [Full Text] [Related]

  • 8. Obstructive Sleep Apnea: A Prediction Model Using Supervised Machine Learning Method.
    Keshavarz Z, Rezaee R, Nasiri M, Pournik O.
    Stud Health Technol Inform; 2020 Jun 26; 272():387-390. PubMed ID: 32604683
    [Abstract] [Full Text] [Related]

  • 9. 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; 26(1):298-317. PubMed ID: 30696334
    [Abstract] [Full Text] [Related]

  • 10. 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 30; 24(9):e39452. PubMed ID: 36178720
    [Abstract] [Full Text] [Related]

  • 11. 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
    [Abstract] [Full Text] [Related]

  • 12. 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 Oct 19; 7():1353469. PubMed ID: 38817683
    [Abstract] [Full Text] [Related]

  • 13. 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 19; 161(3):807-817. PubMed ID: 34717928
    [Abstract] [Full Text] [Related]

  • 14. Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.
    Yook S, Kim D, Gupte C, Joo EY, Kim H.
    Sleep Med; 2024 Feb 19; 114():211-219. PubMed ID: 38232604
    [Abstract] [Full Text] [Related]

  • 15. Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea.
    Pépin JL, Letesson C, Le-Dong NN, Dedave A, Denison S, Cuthbert V, Martinot JB, Gozal D.
    JAMA Netw Open; 2020 Jan 03; 3(1):e1919657. PubMed ID: 31968116
    [Abstract] [Full Text] [Related]

  • 16. A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity.
    Wang KJ, Chen KH, Huang SH, Teng NC.
    J Med Syst; 2016 Apr 03; 40(4):110. PubMed ID: 26932370
    [Abstract] [Full Text] [Related]

  • 17. Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.
    Monna F, Ben Messaoud R, Navarro N, Baillieul S, Sanchez L, Loiodice C, Tamisier R, Joyeux-Faure M, Pépin JL.
    Sleep Med; 2022 Jul 03; 95():76-83. PubMed ID: 35567881
    [Abstract] [Full Text] [Related]

  • 18. Predicting Polysomnography Parameters from Anthropometric Features and Breathing Sounds Recorded during Wakefulness.
    Elwali A, Moussavi Z.
    Diagnostics (Basel); 2021 May 19; 11(5):. PubMed ID: 34069566
    [Abstract] [Full Text] [Related]

  • 19. 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
    [Abstract] [Full Text] [Related]

  • 20. Split-Night Polysomnography Overestimates Apnea-Hypopnea Index in High-Risk Professions.
    Rouse JK, Shirley SR, Holley AB, Mysliwiec V, Walter RJ.
    Mil Med; 2019 May 01; 184(5-6):e137-e140. PubMed ID: 30462265
    [Abstract] [Full Text] [Related]

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