Blockchain-based collaborative data analysis framework for distributed medical knowledge extraction

To ensure the privacy preservation and transparent use of regulated medical big data at decentralized and distributed medical institutions, this paper proposes a blockchain-based collaborative data analysis framework to realize multiparty secure data sharing and cooperative medical knowledge extraction through a transparent and regulatory machine learning approach. A smart contract is employed on the blockchain as the underlying technique to realize autonomous control and transparent regulation of closed-loop data acquisition and analysis. Considering the execution complexity of smart contracts for analysis collaboration, Petri net is adopted to formulize the workflows of smart contracts, and it acts as the underlying on-chain learning (OcL) approach. Finally, an experimental case study is conducted using real-life medical data to verify and evaluate the effectiveness and efficiency of our framework. A prototype system is established to demonstrate the real-life distributed knowledge extraction demand of our cooperating company. Four groups of experiments are designed and conducted to determine the effectiveness and efficiency of the learning process. The results show that the proposed framework significantly outperforms federated learning (FL) in terms of accuracy on small datasets, where the framework achieves an accuracy of 55.050% compared to FL. Meanwhile, the framework exhibits superior convergence in loss compared to FL, with a difference of 76.663%. In the case of big datasets, the framework achieves a faster completion of model training by 58.883%, with lower CPU utilization by 44.023% and lower memory utilization by 16.227% compared to FL.