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

ABSTRACT:

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

Methods

We analyzed polysomnography (PSG) and self-reported clinical information from 1,922 patients tested in our clinical sleep laboratory. We used SLIM and 7 state-of-the-art classification methods to produce predictive models for OSA screening using features from: (i) self-reported symptoms; (ii) self-reported medical information that could, in princi

SUBMITTER: Ustun B 

PROVIDER: S-EPMC4751423 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature