Deep Learning for Clinical and Health Informatics
Amit Kumar Tyagi, Meghna Manoj Nair · 2021
In the recent century, two concepts “Deep Learning” (DL) and “Blockchain technology” have received much attention from around the globe. Today's DL [a superset of artificial neural networks, subset of Machine Learning (ML)] is being used rapidly in many sectors/fields, i.e., in methodological development and practical applications. DL offers many computational models which capture unfinished frameworks of massive data size, complementing majority of the hardware components, however it still faces a few challenges. Data analytics has gained extreme importance over the years and more data need to be analyzed to produce efficient results, leading to ML in HI. DL has proven to be a powerful tool under ML with many features and attributes. Similarly, DL is used in Medicare/bio-medical applications or biomedical informatics, i.e., clinical and HI. Hence, this chapter discusses the use of DL for imaging, i.e., clinical and HI, with a systematic review/critical analysis of the relative merit, and potential pitfalls of the technique as well as its future prospects. This chapter discusses many key applications of DL in the fields of translational bioinformatics, bio-medical imaging, pervasive sensing, medical informatics, and public health including several essential issues, challenges, or opportunities in the same.