This research abstract examines the ability to utilize Dynamic Phenotype Learning (DPL) as an innovative machine learning technique to identify OSA subtypes that can better predict clinical risk and success with therapies.
In this study, we demonstrate how AI methodologies can be utilized together with existing EMR data to create new screening tools to increase diagnostic sensitivity and facilitate discovery of preclinical OSA phenotypes.
In this study, we show deep neural networks (a subset of machine learning) can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings.
Today, we will be discussing AI and sleep medicine, the current relationship between them and how AI might transform sleep medicine going forward.