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
generation of analytical, data driven models based on machine
learning in health informatics. Deep learning, a technique with
its foundation in artificial neural networks, is emerging in recent
years as a powerful tool for machine learning, promising to
reshape the future of artificial intelligence. Rapid improvements
in computational power, fast data storage and parallelization have
also contributed to the rapid uptake of the technology in addition
to its predictive power and ability to generate automatically
optimized high-level features and semantic interpretation from
the input data. This article presents a comprehensive up-to-date
review of research employing deep learning in health informatics,
providing a critical analysis of the relative merit and potential
pitfalls of the technique as well as its future outlook. The paper
mainly focuses on key applications of deep learning in the fields of
translational bioinformatics, medical imaging, pervasive sensing,
medical informatics and public health.