ArticleNature communications2025
Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- Towards convergence of AI and blockchain for personalized medicine in pharmacogenomics.Scientific reports · 2026Article
- Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence.Scientific reports · 2026Article
- VALORIS: One-shot and lossless vertical logistic regression for privacy-protecting multi-site health analytics.Scientific reports · 2026Article
- FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.PloS one · 2026Article
- Application of a machine learning model to predict the estimated primary care patient time consumption.npj health systems · 2026Article
- Based on dual perspectives of management and ethics: exploring challenges and governance approaches for new media applications in psychiatric specialty hospitals.Frontiers in digital health · 2026Article
- A Lightweight Privacy-Enhanced Federated Clustering Algorithm for Edge Computing.Sensors (Basel, Switzerland) · 2025Article
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Quality improvement, clinical research, and patient care can be supported by medical predictive analytics. Predictive models can be improved by integrating more patient records from different healthcare centers (horizontal) or integrating parts of information of a patient from different centers (vertical). We introduce Distributed Cross-Learning for Equitable Federated models (D-CLEF), which incorporates horizontally- or vertically-partitioned data without disseminating patient-level records, to protect patients' privacy. We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios. Using data of more than 15,000 patients with COVID-19 from five University of California (UC) Health medical centers, surgical data from UC San Diego, and heart disease data from Edinburgh, UK, D-CLEF performed close to the centralized solution, outperforming the siloed ones, and equivalent to the federated learning counterparts, but with increased synchronization time. Here, we show that D-CLEF presents a promising accelerator for healthcare systems to collaborate without submitting their patient data outside their own systems.
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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.