EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning

Abstract

Objective Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. Materials and Methods We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. Results We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. Discussion ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but “curious.” Conclusion When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.

iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching

“…2). Blockchain is the distributed ledger technology that laid the foundation of crypto-currencies, and has been proposed for various genomic/healthcare/ biomedical applications [6][7][8][9][10][11][12]. Furthermore, by having immutability without a single-point-of-failure, blockchain technology provides benefits such as transparency, interoperability, and credibility.…”

The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm

“…This centralized approach can create risks such as single-point-of-failure. 5 , 6 Therefore, several existing researches proposed to leverage blockchain, 7–9 a peer-to-peer decentralized architecture, to remove the central server. 5 , 6 , 10–12 Blockchain, a technology originated from financial domain, provides additional desirable technical features such as immutability, provenance, and transparency.…”

“…Although the literature illustrated the rationale and results of adopting blockchain for cross-institutional predictive modeling, the details of the implementation are yet to be described. For example, one of the blockchain-based cross-institutional predictive modeling methods, ExplorerChain, 14 leverages online machine learning on blockchain and was evaluated on healthcare/genomics datasets ( Figure 1 ). Although the advantages/disadvantages of adopting blockchain, the comparison of different architectures/designs, and the equivalent correctness results of ExplorerChain were shown (more details in Healthcare/genomic use cases section), 14 the feasibility study did not include practical considerations while being constructed, and these details could serve as cornerstones for future researchers to develop new algorithms.…”

“… ExplorerChain. 14 Each site maintains an exact copy of the blockchain, and exchanges partially trained machine learning models via the metadata of transactions on-chain. The patient-level data never leave each site to protect patients’ privacy.…”

“…ExplorerChain was applied in 3 use cases, including myocardial infarction, cancer biomarker, and length of hospitalization after surgery. 14 …”