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Related Concept Videos

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Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
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Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using

Jayroop Ramesh1, Niha Keeran1, Assim Sagahyroon1

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.

Healthcare (Basel, Switzerland)
|November 27, 2021
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Summary
This summary is machine-generated.

Machine learning models can identify obstructive sleep apnea (OSA) using routine clinical data. This approach aids in prioritizing patients for further sleep studies, improving accessibility to diagnosis.

Keywords:
electronic health recordsmachine learningobstructivepolysomnographypredictionsleep apnea

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Area of Science:

  • Sleep Medicine
  • Biomedical Informatics
  • Machine Learning

Background:

  • Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by airway obstruction during sleep.
  • Polysomnography is the gold standard for OSA diagnosis but is costly and inaccessible.
  • Effective screening tools are needed to identify individuals requiring further sleep evaluation.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for classifying obstructive sleep apnea (OSA) using routinely collected clinical data.
  • To identify key clinical predictors for OSA classification.
  • To assess the feasibility of using electronic health records for OSA screening.

Main Methods:

  • A machine learning model was trained on 1479 records from the Wisconsin Sleep Cohort dataset.
  • Features included demographics, lab results, physical measurements, sleep history, and comorbidities.
  • Support vector machines (SVM) were optimized using Bayesian Optimization and Genetic Algorithms with five-fold cross-validation.

Main Results:

  • Key predictors for OSA included waist-to-height ratio, neck circumference, BMI, daytime sleepiness, and snoring.
  • The SVM model achieved an accuracy of 68.06%, sensitivity of 88.76%, and specificity of 40.74%.
  • The model demonstrated high negative predictive value (73.33%), suggesting utility in ruling out OSA.

Conclusions:

  • Routinely acquired clinical data can be effectively utilized for OSA classification.
  • Machine learning models show promise for screening and prioritizing patients for polysomnography.
  • This approach can improve the accessibility and efficiency of OSA diagnosis.