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  • About EnsoData
    • Vision
    • Leadership
    • 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
  • EnsoTherapy
  • Resources
    • AI Scoring FAQs
    • Case Studies
    • Webinars
    • White Papers & eBooks
    • Research
    • Sleep Tech Corner
    • Blogs
    • EnsoSleep Scoring Certification
    • Events

Machine Learning

Utilizing machine learning based on multi-modal data to predict PAP adherence in patients with OSA

This study highlights the use of machine learning based on multi-modal data to predict PAP adherence in patients with OSA, presented by Kaiser Permanente and EnsoData Research.

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How will artificial intelligence (AI) advance sleep medicine?

This research abstract addresses various components and methods deployed in AI and covers examples of how AI is used to screen, endotype, diagnose, and treat sleep disorders.

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Machine Learning in Sleep Medicine The AI Revolution

Machine Learning in Sleep Medicine: The AI Revolution

In today’s post, we will be diving back into the future relationship of AI and sleep medicine, and how machine learning will lead to new opportunities in sleep.

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$9M Series A aims to extend adoption of Waveform AI, starting with sleep studies

By simplifying the process for analyzing the human body to accurately diagnose health conditions, we were thrilled to announce on June 10th the close of our $9 million Series A financing round.

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NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning

The study examines a machine learning system, called NEXT, which provides a unique platform for real-world, reproducible active learning research. This paper details the challenges of building the system and demonstrates its capabilities with several experiments.

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Recent Posts
  • EnsoData™ raises $20M Series B to Expand Commercial Team
  • 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
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