{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huo J"],"funding":["National Heart, Lung, and Blood Institute","NHLBI NIH HHS","National Science Foundation"],"pagination":["449-457"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11577832"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(2)"],"pubmed_abstract":["<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."],"journal":["Sleep & breathing = Schlaf & Atmung"],"pubmed_title":["BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea."],"pmcid":["PMC11577832"],"funding_grant_id":["R21 HL159661","2052528","R21HL159661-01"],"pubmed_authors":["Huo J","Li A","Quan SF","Roveda J"],"additional_accession":[]},"is_claimable":false,"name":"BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.","description":"<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.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-26T01:54:52.247Z","creation":"2025-04-06T10:18:15.303Z"},"accession":"S-EPMC11577832","cross_references":{"pubmed":["35482152"],"doi":["10.1007/s11325-022-02629-8"]}}