{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:56:09Z","timestamp":1769554569757,"version":"3.49.0"},"reference-count":65,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,1,16]],"date-time":"2020-01-16T00:00:00Z","timestamp":1579132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["R00HG009680"],"award-info":[{"award-number":["R00HG009680"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["R01HL136835"],"award-info":[{"award-number":["R01HL136835"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["R01GM118609"],"award-info":[{"award-number":["R01GM118609"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"U.S. National Institutes of Health","doi-asserted-by":"crossref","award":["U01EB02385"],"award-info":[{"award-number":["U01EB02385"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]},{"name":"UCSD Academic Senate Research","award":["RG084150"],"award-info":[{"award-number":["RG084150"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>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 \u201cflattened\u201d topology, while real-world research networks may consist of \u201cnetwork-of-networks\u201d 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocz214","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T12:12:04Z","timestamp":1575375124000},"page":"343-354","source":"Crossref","is-referenced-by-count":52,"title":["Privacy-preserving model learning on a blockchain network-of-networks"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8728-4477","authenticated-orcid":false,"given":"Tsung-Ting","family":"Kuo","sequence":"first","affiliation":[{"name":"UCSD Health Department of Biomedical Informatics, University of 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