Using AI to Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions
This research demonstrates a novel application for AI to aid clinicians in maintaining CPAP adherence across identifiable phenotypes.
Using Novel EEG Phenotypes and Artificial Intelligence to Estimate OSA Severity
This research demonstrated a promising opportunity to estimate OSA severity with a host of EEG study types using applied artificial intelligence.
Computational Phenotyping In CPAP Therapy: Using Interpretable Physiology‑Based Machine Learning Models To Predict Therapeutic CPAP Pressures
This research highlights how interpretable machine learning models show strong promise as another means for determining therapeutic CPAP pressures.
A Cross-Validation Approach to Inter-Scorer Reliability Assessment
This work demonstrates that consensus-based reference for sleep study analysis may be constructed and used for Inter-Scorer Reliability (ISR) assessments to enable measurement of scoring agreement with greater reproducibility.
Computational Phenotyping in Polysomnography: Using Interpretable Physiology-Based Machine Learning Models to Predict Health Outcomes
In this study, we utilize a Computational Phenotyping approach using Polysomnography (PSG) data to predict adverse health outcomes based on common clinical variables and interpretable physiological features, providing a clear explanation as to why each estimation is made.
Sleep Apnea: A Review of Diagnostic Sensors, Algorithms, and Therapies
This article reviews the current engineering approaches (including AI and machine learning) for the detection and treatment of sleep apnea.
A Modeling Study on Inspired CO2 Rebreathing Device for Sleep Apnea Treatment by Means of CFD Analysis and Experiment
In this study, we present the device design, simulation, and measurement results of a therapy device that potentially prevents sleep apnea by slightly increasing inspired CO2 through added dead space (DS).
Sleep Scoring Automation Via Large Scale Machine Learning
In this work, we present a large-scale machine learning analysis of a multi-site, 5793 patient dataset, demonstrating strong performance in SDB event classification.
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.