SynthesisJCO clinical cancer informatics2020
Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care.
Synthesis in JCO clinical cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prediction of PD-L1 expression in nonsmall cell lung cancer using artificial intelligence models based on radiomics: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
- Protecting Informational Self-determination in AI-driven Precision Oncology: Privacy, Ethics, and Governance Challenges.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026Article
- Predicting the progression risk of chronic obstructive pulmonary disease in high-risk individuals based on whole-lung radiomics and deep learning: a multicenter study.Journal of thoracic disease · 2026Article
- An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.Nature communications · 2026Article
- Comparing decentralized machine learning and AI clinical models to local and centralized alternatives: a systematic review.NPJ digital medicine · 2026Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- The Role of PET Image Quantification in Modern Radiation Therapy.PET clinics · 2025Review
- A privacy-preserving dependable deep federated learning model for identifying new infections from genome sequences.Scientific reports · 2025Article
- Leveraging machine learning in nursing: innovations, challenges, and ethical insights.Frontiers in digital health · 2025Review
- Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.Computational and structural biotechnology journal · 2024Review
- Foundation model-driven distributed learning for enhanced retinal age prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- FHBF: Federated hybrid boosted forests with dropout rates for supervised learning tasks across highly imbalanced clinical datasets.Patterns (New York, N.Y.) · 2024Article
- Federated Abnormal Heart Sound Detection with Weak to No Labels.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Advancing healthcare through data: the BETTER project's vision for distributed analytics.Frontiers in medicine · 2024Article
- WebQuorumChain: A web framework for quorum-based health care model learning.Informatics in medicine unlocked · 2024Article
- Integration of migrant and refugee data in health information systems in Europe: advancing evidence, policy and practice.The Lancet regional health. Europe · 2023Review
- Infrastructure tools to support an effective Radiation Oncology Learning Health System.Journal of applied clinical medical physics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
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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.