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  • About EnsoData
    • Vision
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    • Culture
    • DEI
  • EnsoSleep
    • EnsoSleep for Health Systems
    • Sleep Study Management
    • AI Sleep Scoring
    • ePrescribing
    • Total Sleep Time
    • Pricing
    • Customer Testimonials
  • EnsoSleep PPG
    • Celeste+
    • Remote Physiological Monitoring
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  • Resources
    • AI Scoring FAQs
    • Case Studies
    • Webinars
    • White Papers & eBooks
    • Research
    • Sleep Tech Corner
    • Blogs
    • EnsoSleep Scoring Certification
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Deep Learning and Sleep

Deep Learning Classification of Future PAP Adherence based on CMS and other Adherence Criteria

This research study shows how AI can deliver strong predictive performance for PAP adherence within the first few weeks of therapy, enabling early PAP intervention or transition to alternative therapies sooner in the process and improving patient outcomes.

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Recent Posts
  • Is Brain Age Malleable to Sleep Apnea Therapy? An Exploratory Positive Airway Pressure Titration and Machine Learning-based Brain Age Study
  • Clinical Validation of ECG-Based Obstructive Sleep Apnea Screening Using Machine Learning
  • Evaluating the Impact of Multi-Night Home Sleep Apnea Testing for Obstructive Sleep Apnea Diagnosis
  • AI-enabled Narcolepsy Type-1 Screening with PPG: a Proof-of-Concept Study
  • EnsoData™ Appoints Chief Commercial Officer, Bobby Cockrill, MBA
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