Invention:
BASH-GN is a machine learning-based questionnaire designed to classify the risk of obstructive sleep apnea (OSA) by considering various risk factor subtypes. Compared with other commonly used OSA screening tools, the BASH-GN questionnaire demonstrated superior performance, making it a more effective option for identifying at-risk individuals. This questionnaire makes the diagnostic process easier and helps healthcare providers take timely actions, improving patient outcomes and reducing the long-term health risks associated with untreated sleep apnea.
Background:
Sleep apnea is a severe sleep disorder characterized by repeated interruptions in breathing during sleep, often resulting in loud snoring and persistent tiredness. Current solutions like the Four-Variable, Epworth Sleepiness Scale, Berlin, and STOP-BANG questionnaires use basic clinical criteria and do not consider various OSA risk factor subtypes. BASH-GN stands out because it uses machine learning to analyze a wider range of risk factors, offering a more customized and precise assessment for overall improved screening accuracy.
Applications:
- Diagnostic for obstructive sleep apnea risk
Advantages:
- Outperforms standard screening questionnaires
- Effective at identifying at-risk individuals for obstructive sleep apnea
- Rapid delivery of results
- Easy to use
- Earlier diagnosis improves patient outcomes
