Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms | Zendy

Berk Ustun | Zendy; M. Brandon Westover | Zendy; Cynthia Rudin | Zendy; Matt T. Bianchi | Zendy

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Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms

Author(s) -

Berk Ustun,

M. Brandon Westover,

Cynthia Rudin,

Matt T. Bianchi

Publication year - 2016

Publication title -

journal of clinical sleep medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.529

H-Index - 92

eISSN - 1550-9397

pISSN - 1550-9389

DOI - 10.5664/jcsm.5476

Subject(s) - medicine , polysomnography , medical history , medical record , obstructive sleep apnea , sleep apnea , sleep medicine , diagnosis code , demographics , clinical history , health records , electronic health record , apnea , sleep disorder , health care , population , psychiatry , insomnia , demography , environmental health , sociology , economics , economic growth

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 based on routinely available medical information would be superior to one based solely on patient-reported sleep-related symptoms.

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