ArticleSleep & breathing = Schlaf & Atmung2023
BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.
Article in Sleep & breathing = Schlaf & Atmung, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Obstructive sleep apnea: Proposed scoring system.World journal of methodology · 2026Review
- Screening obstructive sleep apnea using clinical questionnaires and the triglyceride-glucose index: a cardiovascular risk-based approach.NPJ primary care respiratory medicine · 2026Article
- A novel machine learning model for screening the risk of obstructive sleep apnea using craniofacial photography with questionnaires.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Advancements in Obstructive Sleep Apnea Diagnosis and Screening Through Artificial Intelligence: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Prediction of early myocardial damage in obstructive sleep apnea patients using combined logistic regression and QUEST decision tree models.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2025Article
- Advances in Machine Learning Prediction Models for the Screening of Obstructive Sleep Apnea in Adults.Nature and science of sleep · 2025Review
- Predicting the Risk of Sleep Disorders Using a Machine Learning-Based Simple Questionnaire: Development and Validation Study.Journal of medical Internet research · 2023Article
- Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea.Medicina (Kaunas, Lithuania) · 2022Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to develop a machine learning-based questionnaire (BASH-GN) to classify obstructive sleep apnea (OSA) risk by considering risk factor subtypes.
methodsParticipants 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.
resultsWe 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 .
conclusionConsidering OSA subtypes when evaluating OSA risk may improve the accuracy of OSA screening.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.