Big data analytics in medical engineering and healthcare: methods, advances and challenges

Abstract

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

Keywords:

• Big data analytics
• big data
• medical engineering
• healthcare
• deep learning
• privacy
• cybersecurity
• blockchain

Acknowledgements

The authors thank Technology & Healthcare Solutions, Inc. (A non-profit research and consulting company in Mississippi, USA) for support.

Disclosure statement

Any financial interest or any conflict of interest does not exist.