Integrated Blockchain-Deep Learning Approach for Analyzing the Electronic Health Records Recommender System

Area of Science:

• Computer Science
• Medical Informatics
• Data Security

Background:

• Blockchain technology offers a decentralized, immutable, and secure ledger for data storage.
• Deep learning excels in analyzing complex datasets, finding applications in various computational fields.
• Electronic Health Records (EHR) are crucial for patient medical documentation and inter-organizational data sharing.

Purpose of the Study:

• To propose an integrated blockchain-deep learning environment for analyzing Electronic Health Records (EHR).
• To leverage deep learning for analyzing secure EHR data stored on a blockchain.
• To develop a system for sending automated patient alerts based on EHR analysis.

Main Methods:

• Developing an integrated environment combining blockchain and deep learning.
• Utilizing a deep learning algorithm as an agent to process EHR data stored on the blockchain.
• Implementing a system to send alerts to patients via their registered mobile numbers.

Main Results:

• The proposed system demonstrates the feasibility of analyzing secure EHR data using deep learning on a blockchain.
• The integration enables automated and personalized patient reminders for consultations and diet charts.
• Enhanced data security and privacy are achieved through the use of blockchain technology.

Conclusions:

• The integration of blockchain and deep learning offers a secure and efficient platform for EHR analysis.
• This approach has the potential to improve patient engagement and healthcare management through timely alerts.
• Future work can explore further applications of this integrated environment in personalized medicine.