BASH-GN: a new machine learning–derived questionnaire for screening obstructive sleep apnea

Huo, Jiayan; Quan, Stuart F.; Roveda, Janet; Li, Ao

doi:10.1007/s11325-022-02629-8

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…This explains the superior performance of SLEEPS in comparison to previously developed OSA or insomnia screening questionnaires that were based on LR. Furthermore, we found that distinguishing COMISA from OSA is critical for accurate prediction (Table S2 in Multimedia Appendix 1 ), unlike previous studies [ 25 , 26 , 30 , 31 , 33 , 35 ] where COMISA and OSA were not distinguished. Specifically, the model’s performance decreased when merging the COMISA and OSA labels.…”

Section: Discussioncontrasting

confidence: 86%

“…In contrast to previous OSA screening studies [ 25 , 26 , 30 , 31 , 33 , 35 ], we separated COMISA from OSA. To investigate the importance of this separation, we merged COMISA with OSA to create a new OSA label and retrained the XGBoost model predicting this label using the SMC training set.…”

Section: Resultsmentioning

confidence: 99%

“…The 9 features we used are easy to answer, unlike those in previously developed questionnaires [ 25 , 26 , 30 , 31 , 33 , 35 ], which require information such as observed breathing cessation and systolic and diastolic blood pressure. Despite using only simple items, SLEEPS showed high accuracy in predicting the risk of sleep disorders and outperformed previously developed OSA or insomnia screening questionnaires [ 34 , 35 ]. The use of XGBoost contributed to this improved performance, as other popular classification machine learning algorithms showed poorer performance (Tables S4 and S5 in Multimedia Appendix 1 ).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, these questionnaires require observed breathing cessation [ 25 , 26 ], blood pressure measurements [ 30 ], and neck circumference measurements [ 28 ], which are difficult for the general public to measure. Furthermore, their prediction accuracy is not satisfactory (eg, area under the receiver operating characteristics curve [AUROC]<0.79) [ 34 , 35 ]. Hence, there is a growing demand in clinical settings for a single questionnaire that can accurately predict the risk of all 3 sleep disorders.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the underrecognition of these diseases, the time-consuming nature of sleep monitoring required for an appropriate diagnosis [23,24], and patient reluctance to undergo cumbersome and expensive PSG tests. To address this issue, simple questionnaires have been developed to predict the risk of OSA or insomnia [25][26][27][28][29][30][31][32][33][34][35]. To the best of our knowledge, there is currently no single questionnaire available that can differentially diagnose OSA, COMISA, and insomnia.…”

Section: Introductionmentioning

confidence: 99%

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Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Ha,

Choi,

Lee

et al. 2023

J Med Internet Res

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Background Sleep disorders, such as obstructive sleep apnea (OSA), comorbid insomnia and sleep apnea (COMISA), and insomnia are common and can have serious health consequences. However, accurately diagnosing these conditions can be challenging as a result of the underrecognition of these diseases, the time-intensive nature of sleep monitoring necessary for a proper diagnosis, and patients’ hesitancy to undergo demanding and costly overnight polysomnography tests. Objective We aim to develop a machine learning algorithm that can accurately predict the risk of OSA, COMISA, and insomnia with a simple set of questions, without the need for a polysomnography test. Methods We applied extreme gradient boosting to the data from 2 medical centers (n=4257 from Samsung Medical Center and n=365 from Ewha Womans University Medical Center Seoul Hospital). Features were selected based on feature importance calculated by the Shapley additive explanations (SHAP) method. We applied extreme gradient boosting using selected features to develop a simple questionnaire predicting sleep disorders (SLEEPS). The accuracy of the algorithm was evaluated using the area under the receiver operating characteristics curve. Results In total, 9 features were selected to construct SLEEPS. SLEEPS showed high accuracy, with an area under the receiver operating characteristics curve of greater than 0.897 for all 3 sleep disorders, and consistent performance across both sets of data. We found that the distinction between COMISA and OSA was critical for accurate prediction. A publicly accessible website was created based on the algorithm that provides predictions for the risk of the 3 sleep disorders and shows how the risk changes with changes in weight or age. Conclusions SLEEPS has the potential to improve the diagnosis and treatment of sleep disorders by providing more accessibility and convenience. The creation of a publicly accessible website based on the algorithm provides a user-friendly tool for assessing the risk of OSA, COMISA, and insomnia.

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Section: Discussioncontrasting

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Ha,

Choi,

Lee

et al. 2023

J Med Internet Res

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End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

Arslan,

Ulutas,

Köksal

et al. 2023

Biocybernetics and Biomedical Engineering

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Risk and Uncertainty Communication in Deployed AI-based Clinical Decision Support Systems: A scoping review

Gray,

Page,

Buchan

et al. 2024

Preprint

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Clinical decision support systems (CDSS) employing data-driven technology such as artificial intelligence, machine- and statistical-learning are increasingly deployed in healthcare settings. These systems often provide clinicians with diagnostic, prognostic, or risk scores modelled from curated patient-level data and frequently involve iterative and non-deterministic optimisation of flexible, parameterised models. All of these data and algorithms have uncertainties associated with them that should be taken into account when used to support clinical decisions at the patient level. This scoping review aims to describe the literature on how deployed data-driven CDSSs present information about uncertainty to their intended users. We describe common clinical applications of CDSSs, characterise the decisions that are being supported, and examine how the CDSS provides outputs to end users, including uncertainty at the individual patient level, as well as indirect measures such as CDSS performance against a reference standard. We conclude with a discussion and recommendations on how CDSS development can be improved.

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BASH-GN: a new machine learning–derived questionnaire for screening obstructive sleep apnea

Abstract: doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

Cited by 7 publications

References 29 publications

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

Predicting the Risk of Sleep Disorders Using a Machine Learning–Based Simple Questionnaire: Development and Validation Study

End-to end decision support system for sleep apnea detection and Apnea-Hypopnea Index calculation using hybrid feature vector and Machine learning

Risk and Uncertainty Communication in Deployed AI-based Clinical Decision Support Systems: A scoping review

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