Advanced Blockchain Logging for Health Data Integrity and Security

The proliferation of remote health monitoring systems, making data security, integrity, and real-time analysis a primary issue. Sensitive health data have to be protected from being tampered with, accessed improperly, and from future attacks. Accountability and traceability for processes applied in such systems are inadequate when using traditional measures. This project suggests a cutting-edge solution through the combination of Blockchain Technology, Long Short-Term Memory (LSTM) networks, and Deep Learning mechanisms. The platform leverages Blockchain to offer an immutable and clear record for every interaction, facilitating data integrity and traceability. The LSTM networks process temporal health data, making it possible to gain deeper insight into patient patterns of health progression over time. Deep Learning methods are used for anomaly detection, providing a higher level of security and data reliability. The aim is to develop an architecture that not only protects patient health information but also improves the interpretation and accuracy of telemedicine remote monitoring systems, resulting in improved patient care.