Evidence map›Paper›PMID 30892656›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2019

Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.

Tsung-Ting Kuo, Rodney A Gabriel, Lucila Ohno-Machado

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Blockchain in Health Information Systems: A Systematic Review.International journal of environmental research and public health · 2024
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  15. Detecting model misconducts in decentralized healthcare federated learning.International journal of medical informatics · 2022
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  20. Blockchain in Healthcare: Insights on COVID-19.International journal of environmental research and public health · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tsung-Ting KuoUCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.
Rodney A GabrielUCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.
Lucila Ohno-MachadoUCSD Health Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.

Funding

Decentralized differentially-private methods for dynamic data release and analysisR01GM118609 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2017 to 2020
$2.3M
BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacyR00HG009680 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KUO, TSUNG-TING · 2019 to 2021
$849k
BECKON - Block Estimate Chain: creating Knowledge ON demand & protecting privacyK99HG009680 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI KUO, TSUNG-TING · 2017 to 2018
$176k
NHGRI NIH HHS K99 HG009680NHGRI NIH HHS R00 HG009680NIGMS NIH HHS R01 GM118609
6 · The paper itself

Abstract

objectiveDecentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the "server" role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way. MATERIALS AND

methodsWe propose a framework that includes both server and "client" roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time.

resultsGloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model. DISCUSSION: Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications.

conclusionsOur framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

Indexed as

BlockchainConfidentialityInformation DisseminationInformation SystemsMachine LearningAlgorithmsComputer Communication NetworksDecision Support Systems, ClinicalGenomicsHealth Information SystemsHumansModels, Theoreticalbatch machine learningblockchain distributed ledger technologyclinical information systemsdecision support systemsprivacy-preserving predictive modeling

Identifiers

PMID30892656
PMCPMC7787356

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.