How will artificial intelligence (AI) advance sleep medicine?
Nathaniel F. Watson1 • Cathy Goldstein2 • Sam Rusk3 • Chris Fernandez3 Download the Study Abstract Artificial intelligence (AI) combines
Clinical Validation of AI Scoring in Adult and Pediatric Clinical PSG Samples Compared to Prospective, Double-Blind Scoring Panel
Chris R. Fernandez, MS1 • Sam Rusk, BS1 • Yoav N. Nygate, MSc1 • Nick Glattard, MS1 • Fred Turkington,
Clinical Validation of AI Analysis of PPG Based Sleep-Wake Staging, Total Sleep Time, and Respiratory Rate
Nathaniel F. Watson, MD, MSc1 • Chris R. Fernandez, MS2 • Sam Rusk, BS2 • Yoav N. Nygate, MSc2 •
Redefining Positive Airway Pressure (PAP) Adherence Phenotypes Utilizing Deep Neural Networks and Unsupervised Clustering
Yoav N. Nygate, MS1 • Sam Rusk, BS1 • Chris R. Fernandez, MS1 • Nick Glattard, MS1 • Nathaniel F.
Dynamic Phenotype Learning: A Novel Machine Learning Approach To Develop And Discover New OSA Sub-Types
Chris R. Fernandez, MS1 • Sam Rusk, BS1 • Yoav N. Nygate, MS1 • Nick Glattard, MS1 • Jessica Arguelles,
Evaluation of Electronic Medical Record Artificial Intelligence Screening Tools for Undiagnosed OSA
Sam Rusk, BS • Yoav N. Nygate, MSc • Fred Turkington, BS • Chris R. Fernandez, MS • Nick Glattard,
EEG-Based Deep Neural Network Model for Brain Age Prediction and its Association with Patient Health Conditions
Yoav N. Nygate, MS, Sam Rusk, BS, Chris R. Fernandez, MS, Nick Glattard, MS, Jessica Arguelles, BS, Jiaxiao M. Shi,
Using AI to Predict Future CPAP Adherence and the Impact of Behavioral and Technical Interventions
William Hevener, RPSGT • Bretton Beine, BA, RPSGT • John Woodruff • Dominic Munafo, MD • Chris Fernandez, MS1 •
Using Novel EEG Phenotypes and Artificial Intelligence to Estimate OSA Severity
Chris Fernandez1,2 • Sam Rusk, BS1,2 • Nick Glattard, MS1,2 • David Piper, BS1 • Jonathan Solis, BS1 • Brock
“Be thoughtful, be mindful, and take your time in situations where bias could be present.”
Andrea Ramberg, RPSGT, CCSH EnsoData Clinical Director
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