Research Article
Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review
@ARTICLE{10.4108/eai.23-3-2021.169072, author={Nawaz Ejaz and Raza Ramzan and Tooba Maryam and Shazia Saqib}, title={Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={8}, number={32}, publisher={EAI}, journal_a={SIS}, year={2021}, month={3}, keywords={Electronic medical records, big data, Security, Block-chain, Deep learning}, doi={10.4108/eai.23-3-2021.169072} }- Nawaz Ejaz
Raza Ramzan
Tooba Maryam
Shazia Saqib
Year: 2021
Big Data Management of Hospital Data using Deep Learning and Block-chain Technology: A Systematic Review
SIS
EAI
DOI: 10.4108/eai.23-3-2021.169072
Abstract
The main recompenses of remote and healthcare are sensor-based medical information gathering and remote access to medical data for real-time advice. The large volume of data coming from sensors requires to be handled by implementing deep learning and machine learning algorithms to improve an intelligent knowledge base for providing suitable solutions as and when needed. Electronic medical records (EMR) are mostly stored in a client-server database and are supported by enabling technologies like Internet of Things (IoT), Sensors, cloud, big data, Deep Learning, etc. It is accessed by several users involved like doctors, hospitals, labs, insurance providers, patients, etc. Therefore, data security from illegal access is crucial especially to manage the integrity of data. In this paper, we describe all the basic concepts involved in management and security of such data and proposed a novel system to securely manage the hospital’s big data using Deep Learning and Block-Chain technology.
Copyright © 2021 Nawaz Ejaz et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
