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Deep Learning for Clinical Decision Support Systems: A Review from the Panorama of Smart Healthcare

  • E. Sandeep KumarEmail author
  • Pappu Satya Jayadev
Chapter
Part of the Studies in Big Data book series (SBD, volume 68)

Abstract

Innovations in Deep learning (DL) are tremendous in the recent years and applications of DL techniques are ever expanding and encompassing a wide range of services across many fields. This is possible primarily due to two reasons viz. availability of massive amounts of data for analytics, and advancements in hardware in terms of storage and computational power. Healthcare is one such field that is undergoing a major upliftment due to pervasion of DL in a large scale. A wide variety of DL algorithms are being used and being further developed to solve different problems in the healthcare ecosystem. Clinical healthcare is one of the foremost areas in which learning algorithms have been tried to aid decision making. In this direction, combining DL with the existing areas like image processing, natural language processing, virtual reality, etc., has further paved way in automating and improving the quality of clinical healthcare enormously. Such kind of intelligent decision making in healthcare and clinical practice is also expected to result in holistic treatment. In this chapter, we review and accumulate various existing DL techniques and their applications for decision support in clinical systems. There are majorly three application streams of DL namely image analysis, natural language processing, and wearable technology that are discussed in detail. Towards the end of the chapter, a section on directions for future research like handling class imbalance in diagnostic data, DL for prognosis leading to preventive care, data privacy and security would be included. The chapter would be a treat for budding researchers and engineers who are aspiring for a career in DL applied healthcare.

Keywords

Machine learning Deep learning Smart healthcare Clinical decision support system 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Telecommunication EngineeringM.S. Ramaiah Institute of TechnologyBengaluruIndia
  2. 2.Department of Electrical EngineeringIIT MadrasChennaiIndia

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