Medical big data holds tremendous promise to transform the medical industry but is currently facing big challenges. The abundant medical data amassed from various sources is extremely diverse in type, format, and context. As a result, traditional databases are not able to handle medical big data efficiently. At the moment storing, recording, and sharing medical data securely among diverse systems and applications is a costly and tedious affair.
According to the statistics, less than 10% of the health care organizations share their medical data with any other party. Thus most of the medical data is lying in silos. This is happening because existing systems lack interoperability. Hence transmitting, retrieving, cleaning, and analyzing medical data is an enormously challenging task. CyberVein aims to address all these challenges with a one-stop solution.
Efficient data processing
Problem 1: Diverse sources of medical data
The CyberVein medical data center offers its users a powerful data collection layer. The layer utilizes tools like Oracle Golden Gate (OGG) to synchronize data between heterogeneous environments. Furthermore, it uses ETL tools that easily unite multiple data sources. Apart from that the data center offers backup and recovery provision, materialized views, and more.
Problem 2: 3 Vs challenge of medical big data
In the data storage and management layer, CyberVein utilizes DAG (Directed Acyclic Graph). By using DAG for parallel processing of transactions, CyberVein medical data center solves the scalability issues. In theory, a DAG is capable of delivering a throughput of millions of transactions per second.
Problem 3: Value of data for clinical scientific research is suffering
CyberVein medical data center is a powerful computational tool as it utilizes DAG. This improves the data processing efficiency of the system. On top of that it utilizes federated learning(AI) that ensures every user of the platform can derive precise value out of the data. It also makes the system capable of self-learning. Such a set-up allows anyone an opportunity to build a database, smart contract, and DApp to access the database on a parameter basis. The platform users can create their own data ownership rights and data transaction formats. With federated learning, they can also build precise data value conversion capabilities.
A Real-world tested platform
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