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.