Abstract The Precision Medicine Initiative brings tremendous opportunities to speedup
scientific discovery and promote quality improvement in medicine. However, it also raises …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Understanding and using complex, high-dimensional, and heterogeneous biological data
remains a major obstacle in healthcare transformation. Electronic health records, imaging …

With a massive influx of multimodality data, the role of data analytics in health informatics
has grown rapidly in the last decade. This has also prompted increasing interests in the …

Deep learning, a subfield of artificial intelligence, has shown exponential growth in health
care applications. With the improvement of computational power and advances in big data …

Actionable insights and learning from a highly complex biomedical dataset is a key
challenge in smart healthcare. Traditional data processing algorithms fails to provide the …

The types of data most commonly used for machine learning in biomedical research,
including electronic health records, imaging,-omics, sensor data, and medical text, are …

Obtaining information and real-time insights from complicated, high-dimensional, and
diverse biological data is a major problem in healthcare transformation. In current …

The need of data analytics in health informatics for better decision making is a challenging
domain for the past decade. This stimulates more interest of researchers for the design of …

With the development of data acquisition and storage techniques, overwhelming amounts of
data have been emerging in modern healthcare research, including longitudinal health …