Sleep Health

Sleep health research focuses on improving understanding and diagnosis of sleep disorders and their impact on overall health, employing diverse methodologies to achieve this. Current research utilizes advanced machine learning techniques, including deep learning models (like CNNs and LSTMs), statistical methods (e.g., n-gram models), and natural language processing to analyze data from various sources such as wearable sensors, polysomnography, and clinical notes. These efforts aim to develop more accurate and accessible diagnostic tools, personalized interventions, and predictive models for sleep disorders like insomnia and obstructive sleep apnea, ultimately improving patient care and public health outcomes.