Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model

Abstract

In recent times, advanced developments in healthcare sector result in the generation of massive amounts of electronic health records (EHRs). EHR system enables the data owner to control his/her data and share it with designated people. The vast volume of data in the healthcare system makes it difficult for data to ensure security and diagnostic processes. To resolve these issues, this paper develops a new hyperledger blockchain enabled secure medical data management with deep learning (DL)-based diagnosis (HBESDM-DLD) model. The presented model involves distinct stages of operations such as encryption, optimal key generation, hyperledger blockchain-based secure data management, and diagnosis. The presented model allows the user to control access to data, permit the hospital authorities to read/write data, and alert emergency contacts. For encryption, SIMON block cipher technique is applied. At the same time, to improve the efficiency of the SIMON technique, a group teaching optimization algorithm (GTOA) is applied for the optimal key generation of the SIMON technique. Moreover, the sharing of medical data takes place using multi-channel hyperledger blockchain that utilizes a blockchain for storing patient visit data and for the medical institutions to record links for the EHRs saved in external databases. Once the data are decrypted at the receiving end, finally, variational autoencoder (VAE)-based diagnostic model is applied to detect the existence of the diseases. The performance validation of the HBESDM-DLD model takes place on benchmark medical dataset and the results are inspected under various performance measures. The experimental results proves that the HBESDM-DLD methodology is superior to state-of-the-art methods.

Design of Optimal Scheduling Model for Emergency Medical Supplies by Blockchain Technology

“…e huge amount of data in the healthcare system makes it difficult to ensure safety and diagnostic procedures. In order to solve these problems, Sammeta and Parthiban [7] developed a new ultrazipper blockchain to support safe medical data management using deep learning diagnostic models. e presented model involves different stages of operation, such as encryption, optimal key generation, blockchain-based super-blockchain security data management, and diagnosis.…”

Blockchain machine learning based personal health record data analysis with smart decentralization and security enhancement

“…( 1), making 𝐖 𝑥 , i.e., 𝐡 𝑖 = 𝑔(𝐱 𝑖 𝐖 𝐗 ). In this case, 𝐡 𝑖 and 𝑔(⋅) both refer to the same equation, Eq (1).…”

“…( 1) and add an additional dimension to sample 𝐱 𝑖 as follows: 𝐱 𝑖 = [𝑥 𝑖1 , 𝑥 𝑖2 , … , 𝑥 𝑖𝑛−1 , 1]. The outcome of this procedure will be identical to that of utilising bias term in Eq (1).…”

“…Data integrity, security, and privacy concerns can all be addressed more effectively with this emerging technology. Following Satoshi Nakamoto's publication of the Bitcoin white paper [1], the term blockchain gained prominence. Bitcoin's primary function is to facilitate financial transactions without involvement of a trusted third party.…”

Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain

“…Blockchain is collectively maintained and stored by network nodes, constituting a shared distributed trusted database [8][9][10]. The great advantages of blockchain are anonymous, decentralized, traceable, and tamper-resistant [11][12][13][14][15][16][17][18].…”