Research on Intelligent Diagnosis and Treatment of Sleep Apnea Syndrome Driven by Health Big Data

With the advancement of big data technology and artificial intelligence, particularly in deep learning and event prediction techniques, new methods and tools have emerged for the intelligent diagnosis and treatment of sleep apnea syndrome (SAS). This study introduces the application of health big data in SAS diagnosis and treatment, covering data sources, processing methods, and case studies. It then examines the application of deep learning techniques in SAS detection, highlighting key algorithms and their effectiveness. Additionally, it explores event prediction techniques for SAS episode warning, presenting common prediction models and their case studies. The paper summarizes the architecture and functions of intelligent diagnosis and treatment systems, analyzing their practical effectiveness and feasibility, and proposing future research directions. This paper aims to provide a theoretical foundation and practical guidance for the intelligent diagnosis and treatment of SAS, fostering research and application development in this field.