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Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms
STUDY OBJECTIVE: Obstructive sleep apnea (OSA) is a treatable contributor to morbidity and mortality. However, most patients with OSA remain undiagnosed. We used a new machine learning method known as SLIM (Supersparse Linear Integer Models) to test the hypothesis that a diagnostic screening tool ba...
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| Publicado no: | J Clin Sleep Med |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
American Academy of Sleep Medicine
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4751423/ https://ncbi.nlm.nih.gov/pubmed/26350602 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.5664/jcsm.5476 |
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