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Recent Advances in Computer Science and Communications


ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Review Article

An Integrated Approach for Analysis of Electronic Health Records Using Blockchain and Deep Learning

Author(s): Pooja Singhal, Shelly Gupta, Deepak and Jagendra Singh*

Volume 16, Issue 9, 2023

Published on: 20 June, 2023

Article ID: e090523216715 Pages: 10

DOI: 10.2174/2666255816666230509142714

Price: $65


Blockchain is used to assess health records digitally, preserving the security and immutability of the records. The goal of this study is to make it easier for patients to access their medical records and to send them alert messages about important dates for their check-ups, healthy diet, and appointments. To achieve the above-mentioned objective, an integrated approach using Blockchain and Deep learning is initiated. The first approach is Hyperledger Fabric in Blockchain, i.e., private Blockchain, for storing the data in the medically documented ledger, which can be shared among hospitals as well as Health organizations. The second approach is incorporated with a deep learning algorithm. With the help of algorithms, we can analyse the ledger, after which an alert i.e., consultation, health diet, medication, etc., will be sent to the patient’s registered mobile device. The proposed work uses nine features from the dataset; the features are identification number, age, person gender, disease, weight, consultation date, medication, diagnosis, and diet specification. The study is conducted with several features to give accurate results. The integrated model used in this suggested piece of work automates the patient's alert system for a variety of activities. In terms of precision, recall, and F1 score, testing data demonstrate that the LSTM performs better than the other models. By working together with the calendar software on android mobile devices, alert systems can be improved in the future.

Keywords: Blockchain, deep learning, integrated environment, hyperledger fabric, RNN long short-term memory, EHR.

Graphical Abstract
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