biostudies-literatureHuo JNational Heart, Lung, and Blood InstituteNHLBI NIH HHSNational Science Foundation449-457https://www.ebi.ac.uk/biostudies/studies/S-EPMC11577832biostudies-literatureUnknown27(2)<h4>Purpose</h4>This study aimed to develop a machine learning-based questionnaire (BASH-GN) to classify obstructive sleep apnea (OSA) risk by considering risk factor subtypes.<h4>Methods</h4>Participants who met study inclusion criteria were selected from the Sleep Heart Health Study Visit 1 (SHHS 1) database. Other participants from the Wisconsin Sleep Cohort (WSC) served as an independent test dataset. Participants with an apnea hypopnea index (AHI) ≥ 15/h were considered as high risk for OSA. Potential risk factors were ranked using mutual information between each factor and the AHI, and only the top 50% were selected. We classified the subjects into 2 different groups, low and high phenotype groups, according to their risk scores. We then developed the BASH-GN, a machine learning-based questionnaire that consists of two logistic regression classifiers for the 2 different subtypes of OSA risk prediction.<h4>Results</h4>We evaluated the BASH-GN on the SHHS 1 test set (n = 1237) and WSC set (n = 1120) and compared its performance with four commonly used OSA screening questionnaires, the Four-Variable, Epworth Sleepiness Scale, Berlin, and STOP-BANG. The model outperformed these questionnaires on both test sets regarding the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). The model achieved AUROC (SHHS 1: 0.78, WSC: 0.76) and AUPRC (SHHS 1: 0.72, WSC: 0.74), respectively. The questionnaire is available at https://c2ship.org/bash-gn .<h4>Conclusion</h4>Considering OSA subtypes when evaluating OSA risk may improve the accuracy of OSA screening.Sleep & breathing = Schlaf & AtmungBASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.PMC11577832R21 HL1596612052528R21HL159661-01Huo JLi AQuan SFRoveda JfalseBASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.<h4>Purpose</h4>This study aimed to develop a machine learning-based questionnaire (BASH-GN) to classify obstructive sleep apnea (OSA) risk by considering risk factor subtypes.<h4>Methods</h4>Participants who met study inclusion criteria were selected from the Sleep Heart Health Study Visit 1 (SHHS 1) database. Other participants from the Wisconsin Sleep Cohort (WSC) served as an independent test dataset. Participants with an apnea hypopnea index (AHI) ≥ 15/h were considered as high risk for OSA. Potential risk factors were ranked using mutual information between each factor and the AHI, and only the top 50% were selected. We classified the subjects into 2 different groups, low and high phenotype groups, according to their risk scores. We then developed the BASH-GN, a machine learning-based questionnaire that consists of two logistic regression classifiers for the 2 different subtypes of OSA risk prediction.<h4>Results</h4>We evaluated the BASH-GN on the SHHS 1 test set (n = 1237) and WSC set (n = 1120) and compared its performance with four commonly used OSA screening questionnaires, the Four-Variable, Epworth Sleepiness Scale, Berlin, and STOP-BANG. The model outperformed these questionnaires on both test sets regarding the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). The model achieved AUROC (SHHS 1: 0.78, WSC: 0.76) and AUPRC (SHHS 1: 0.72, WSC: 0.74), respectively. The questionnaire is available at https://c2ship.org/bash-gn .<h4>Conclusion</h4>Considering OSA subtypes when evaluating OSA risk may improve the accuracy of OSA screening.2023-01-01T00:00:00Z2023 May2025-04-26T01:54:52.247Z2025-04-06T10:18:15.303ZS-EPMC115778323548215210.1007/s11325-022-02629-8