Big Data in sleep apnoea: Opportunities and challenges.

Area of Science:

• Medical Informatics
• Public Health
• Biostatistics

Background:

• Sleep apnoea is a prevalent, chronic, systemic disease requiring long-term management.
• Accurate patient phenotyping is crucial for optimizing personalized treatment strategies.
• Vast amounts of health-related data are generated from various sources, including electronic health records and connected devices.

Purpose of the Study:

• To review the application of Big Data in understanding sleep-disordered breathing.
• To explore how Big Data can enhance sleep apnoea diagnosis and treatment personalization.
• To discuss the potential of Big Data in informing health policies and resource allocation.

Main Methods:

• Review of existing literature on Big Data applications in sleep apnoea.
• Analysis of data sources including electronic health records, claims data, and data from positive airway pressure (PAP) devices.
• Discussion of statistical approaches, machine learning, and artificial intelligence in Big Data analysis.

Main Results:

• Positive airway pressure (PAP) devices generate substantial data, offering unique opportunities for research.
• Big Data can aid in identifying undiagnosed sleep apnoea and tailoring treatments to individual patient phenotypes.
• Data-driven insights can inform public health policies and resource allocation for sleep apnoea management.

Conclusions:

• Big Data, particularly from PAP devices, holds immense potential for advancing sleep apnoea care.
• Addressing challenges in data management, analysis, and ethical considerations is vital for leveraging Big Data effectively.
• Big Data can overcome limitations of traditional clinical trials, promoting real-world evidence-based medicine for sleep apnoea.