|Title:||Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample||Authors:||Wen-Chi Huang
Chiang, Ambrose A
|Keywords:||machine learning; modeling; obstructive; polysomnography; prediction; sleep apnea||Issue Date:||13-Jul-2020||Journal Volume:||43||Journal Issue:||7||Source:||Sleep||Abstract:||
Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) but it is costly and access is often limited. The aim of this study is to develop a clinically useful support vector machine (SVM)-based prediction model to identify patients with high probability of OSA for nonsleep specialist physician in clinical practice.
|Appears in Collections:||醫學院附設醫院 (臺大醫院)|
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