ArticleJournal of biomedical informatics2013
EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.
Article in Journal of biomedical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.JCO clinical cancer informatics · 2020Pooled it
- Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.Nature communications · 2025Article
- Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts.International journal of medical informatics · 2023Article
- Detecting model misconducts in decentralized healthcare federated learning.International journal of medical informatics · 2022Article
- Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.Methods of information in medicine · 2022Article
- Privacy-preserving quality control of neuroimaging datasets in federated environments.Human brain mapping · 2022Article
- sPLINK: a hybrid federated tool as a robust alternative to meta-analysis in genome-wide association studies.Genome biology · 2022Article
- Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions.SN computer science · 2021Article
- Web-Based Privacy-Preserving Multicenter Medical Data Analysis Tools Via Threshold Homomorphic Encryption: Design and Development Study.Journal of medical Internet research · 2020Article
- The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm.JAMIA open · 2020Article
- Privacy-preserving construction of generalized linear mixed model for biomedical computation.Bioinformatics (Oxford, England) · 2020Article
- EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Privacy-preserving model learning on a blockchain network-of-networks.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.Genetic epidemiology · 2020Article
- Fair compute loads enabled by blockchain: sharing models by alternating client and server roles.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- 'Fit-for-purpose?' - challenges and opportunities for applications of blockchain technology in the future of healthcare.BMC medicine · 2019Article
- A Query Workflow Design to Perform Automatable Distributed Regression Analysis in Large Distributed Data Networks.EGEMS (Washington, DC) · 2018Article
- Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation.JMIR medical informatics · 2018Article
- Three controversies in health data science.International journal of data science and analytics · 2018Article
- PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.