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Technical Challenges for Big Data in Biomedicine and Health: Data Sources, Infrastructure, and Analytics

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Efficient processing of big data relies on distributing computation across computer clusters, enhancing speed and capacity.
Analyzing big datasets can lead to bias, particularly from routine care data, which may result in false-positive findings.
Increasing use of social media data marks a significant shift in the sources contributing to the big data landscape in healthcare.
Future methods must adapt to the requirements of distributed processing while ensuring data confidentiality and integrity.

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Review highlights big data sources and analytics challenges in biomedicine and health, suggesting a need for analytical method transformation.

Holmes et al. (2014) studied this question.

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Review highlights big data sources and analytics challenges in biomedicine and health, suggesting a need for analytical method transformation.