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2019 | OriginalPaper | Chapter

6. The Transparency of Big Data, Data Harvesting and Digital Twins

Authors : Stefan Kendzierskyj, Hamid Jahankhani, Arshad Jamal, Jaime Ibarra Jimenez

Published in: Blockchain and Clinical Trial

Publisher: Springer International Publishing

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Abstract

Computer storage and cloud computing has become more powerful with multiple algorithms running complex data analysis looking at intelligence trends, user behaviour, profiling and ways to make use of these outputs. Added with the artificial intelligence (AI) interaction has meant a new and dynamic method to create models forging analysis to be more clinical, proficient and continually seeking more improvement with the self-learning and intelligent programming of machine learning (ML). In the healthcare sector there is deep interest in collecting, curating the data and making the best use of silo’d data through methods such as blockchain. This can then lead to a multitude of innovations such as precision based medicine, targeting individual variability in genes, their environment, etc. It also means that big data analytics in healthcare is evolving into providing these insights from very large data sets and improving outcomes while reducing costs and inefficiencies. However, there also are some ethical impacts in the process of Digital Twins which can lead to segmentation and discrimination. Or perhaps the data that is automatically collected from healthcare sensors in IoMT and what type of governance are they scrutinized to. It is clear that data is the most important asset of not just an organisation but also to the individual and why the General Data Protection Regulation (GDPR) has taken an important stance in data protection by design and default, that all organisations needs to follow. This chapter aims to highlight some of the concerns.

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Metadata
Title
The Transparency of Big Data, Data Harvesting and Digital Twins
Authors
Stefan Kendzierskyj
Hamid Jahankhani
Arshad Jamal
Jaime Ibarra Jimenez
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-11289-9_6

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