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Support vector machine prediction of obstructive sleep apnea in a large-scale Chinese clinical sample
STUDY OBJECTIVES: 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 o...
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| Pubblicato in: | Sleep |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Oxford University Press
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7355399/ https://ncbi.nlm.nih.gov/pubmed/31917446 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/sleep/zsz295 |
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