Paper · 42961350
Admission Mechanisms for Scalable Federated Health Data Collaboration: Design and Proof-of-Concept Study.
Fenoglio E and 5 others. JMIR Med Inform, 2026.
11 sourcespapers-2026-11-12-0.1.0
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ConductScience Library. Admission Mechanisms for Scalable Federated Health Data Collaboration: Design and Proof-of-Concept Study. Papers, record 42961350. Release papers-2026-11-12-0.1.0, 12 Nov 2026. https://conductscience.org/library/papers/42961350
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Paper
PubMed's record of the paper, with iCite's citation figures. No abstract is shown.
- Title
- Admission Mechanisms for Scalable Federated Health Data Collaboration: Design and Proof-of-Concept Study.
- Journal
- JMIR Med Inform
- Year
- 2026
- Publication types
- Journal Article
- Revised in PubMed
- 2026-11-01
Source PubMed
Topics6
NLM's MeSH indexing of the paper, its major topics first.
| MeSH term | Major |
|---|---|
| Cooperative Behavior | Major topic |
| Federated Learning | |
| Humans | |
| Proof of Concept Study | |
| Digital Health | |
| Pilot Projects |
Source PubMed
Grants0
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Trials0
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Comments and corrections0
PubMed's comments and corrections, NLM's own indexing: retractions first, then expressions of concern, errata and updates, and comments last.
None in this release.
References8
The papers the reference list in PubMed's record names, those in our copy of PubMed, in the list's order.
| Order | PMID | Title | Journal | Year | In Papers | Source |
|---|---|---|---|---|---|---|
| 1 | 36,658,144 | MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification. | Sci Data | 2023 | https://conductscience.org/library/papers/36658144 | PubMed |
| 2 | 39,401,125 | Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study. | JMIR Med Inform | 2024 | https://conductscience.org/library/papers/39401125 | PubMed |
| 3 | 42,582,599 | A system-level analysis of challenges and strategies for scaling artificial intelligence in healthcare: A qualitative study of the NHS AI lab. | Digit Health | 2026 | https://conductscience.org/library/papers/42582599 | PubMed |
| 4 | 33,015,372 | The future of digital health with federated learning. | NPJ Digit Med | 2020 | https://conductscience.org/library/papers/33015372 | PubMed |
| 5 | 32,640,463 | Factors associated with COVID-19-related death using OpenSAFELY. | Nature | 2020 | https://conductscience.org/library/papers/32640463 | PubMed |
| 6 | 36,125,859 | Next-Generation Capabilities in Trusted Research Environments: Interview Study. | J Med Internet Res | 2022 | https://conductscience.org/library/papers/36125859 | PubMed |
| 7 | 30,677,016 | Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. | PLoS Med | 2019 | https://conductscience.org/library/papers/30677016 | PubMed |
| 8 | 32,965,236 | Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model. | JMIR Med Inform | 2020 | https://conductscience.org/library/papers/32965236 | PubMed |
Cited by0
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Genes0
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Drugs0
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Data0
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Data dates
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- Trials
- 2026-11-12
- Gene search
- 2026-11-11
- Pipelines
- 2026-09-18
Sources 11
- ChEMBL, through Open TargetsCC BY-SA 3.0
- ClinicalTrials.govNo license stated
- HGNCCC0
- iCiteCC BY 4.0
- NCBI GenePublic domain (US government)
- NIH RePORTER's grant and paper linksPublic domain
- Open TargetsCC0 1.0
- PMC's ID filePublic domain (US government)
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0
Each source's terms
- ChEMBL, through Open TargetsCC BY-SA 3.0, for the parts from ChEMBL. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- ClinicalTrials.govnone stated
- HGNCCC0: https://www.genenames.org/about/license/
- iCiteCC BY 4.0
- NCBI GeneNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- NIH RePORTER's grant and paper linksPublic domain (NIH RePORTER)
- Open TargetsCC0 1.0. Open Targets asks that users cite its latest paper: Buniello et al. 2025, Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery, Nucleic Acids Research 53(D1):D1467, PMID 39657122. Read on 8 Nov 2026 at https://platform-docs.opentargets.org/licence
- PMC's ID fileNone named for the file. NCBI says information created by or for the US government on its site is in the public domain, and asks that NLM be acknowledged: https://www.ncbi.nlm.nih.gov/home/about/policies/
- PubMedNLM's terms
- PubTator 3Public domain
- UniProtCC BY 4.0. UniProt's licence file: "All databases and documents in the UniProt FTP directory may be copied and redistributed freely, without advance permission, provided that this copyright statement is reproduced with each copy." UniProt asks that users cite its latest paper: The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research 53:D609-D617, https://doi.org/10.1093/nar/gkae1010. Read on 9 Nov 2026 at https://www.uniprot.org/help/license
ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
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