A Semantic-Based Approach for Managing Healthcare Big Data: A Survey

Area of Science:

• Health Informatics
• Computer Science
• Data Science

Background:

• Healthcare generates vast, heterogeneous data from diverse sources (EHRs, labs, wearables).
• Traditional data processing struggles with the volume, velocity, and variety of healthcare big data.
• Existing systems face challenges in extracting meaningful insights from complex health information.

Purpose of the Study:

• To review the state-of-the-art applications of semantic web technologies in the healthcare industry.
• To explore how semantic web can address the challenges of healthcare big data.
• To identify techniques, standards, and viewpoints for converting healthcare data into knowledge.

Main Methods:

• Comprehensive literature review of semantic web applications in healthcare.
• Analysis of existing research on healthcare big data challenges.
• Synthesis of semantic web techniques and standards relevant to health data.

Main Results:

• Semantic web technologies show promise in managing and processing healthcare big data.
• These technologies facilitate the transformation of raw health data into valuable knowledge and intelligence.
• Various semantic web approaches can effectively address the 'big data' challenges in healthcare.

Conclusions:

• Semantic web technologies are crucial for unlocking the potential of healthcare big data.
• Adoption of semantic web standards and techniques can enhance healthcare information systems.
• Further research and implementation are needed to fully leverage semantic web for improved healthcare outcomes.