Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts

Background: Collaborative privacy-preserving modeling across several healthcare institutions allows for the construction of more generalizable predictive models while protecting patient privacy.

Objective: We aim at addressing the site availability issue on a hierarchical network by designing an immutable/transparent/source-verifiable quorum mechanism.

Methods: We developed an approach to combine a hierarchical learning algorithm, a novel Proof-of-Quorum (PoQ) consensus protocol, and a design of blockchain smart contracts. We constructed QuorumChain as an example and evaluated the scenarios of site-unavailability during the initialization and/or iteration phases of the modeling process on three healthcare/genomic datasets.

Results: When one or more sites would become unavailable, HierarchicalChain could not function, whereas QuorumChain improved predictive correctness significantly (the full Area Under the receiver operating characteristic Curve, or AUC, improved from 0.068 to 0.441, all with p-values < 0.001).

Conclusion: By constructing a quorum to continue the modeling process, QuorumChain possesses the capability to tackle the situation of sites being unavailable. It inherits the capability of learning on network-of-networks, improves learning continuity, and provides data/software immutability, transparency, and provenance, which can be important in expediting clinical research.