Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 27, Issue 3, Pages 343-354Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocz214
Keywords
blockchain distributed ledger technology; privacy-preserving predictive modeling; hierarchical network; clinical information systems; decision support systems
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Funding
- U.S. National Institutes of Health (NIH) [R00HG009680, R01HL136835, R01GM118609, U01EB02385]
- UCSD Academic Senate Research Grant [RG084150]
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Objective: To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a flattened topology, while real-world research networks may consist of network-of-networks which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. Materials and Methods: We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. Results: HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. Discussion: HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. C Conclusion: We demonstrated the potential of utilizing the information from the hierarchical network-ofnetworks topology to improve prediction.
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