ArticleJCO clinical cancer informatics2021
Cloud-Based Federated Learning Implementation Across Medical Centers.
Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.Briefings in bioinformatics · 2026Review
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- Plying potency assays for immunotherapy of solid tumors.Frontiers in immunology · 2026Review
- Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Advancing breast, lung and prostate cancer research with federated learning. A systematic review.NPJ digital medicine · 2025Article
- Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.Nature communications · 2024Article
- PPFL: A personalized progressive federated learning method for leveraging different healthcare institution-specific features.iScience · 2024Article
- Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline.JMIR medical informatics · 2024Article
- Federated learning for medical image analysis: A survey.Pattern recognition · 2024Article
- Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities.International journal of environmental research and public health · 2024Review
- Survey of Medical Applications of Federated Learning.Healthcare informatics research · 2024Article
- Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Clinical Informatics Approaches to Facilitate Cancer Data Sharing.Yearbook of medical informatics · 2023Review
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
- Artificial Intelligence in Congenital Heart Disease: Current State and Prospects.JACC. Advances · 2022Review
- Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit.Advances in chronic kidney disease · 2022Review
- Performance and Information Leakage in Splitfed Learning and Multi-Head Split Learning in Healthcare Data and Beyond.Methods and protocols · 2022Article
- FIDChain: Federated Intrusion Detection System for Blockchain-Enabled IoT Healthcare Applications.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
purposeBuilding well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform. MATERIALS AND
methodsUsing two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments.
resultsThe cyclically trained ANN showed a 3% increase in performance, a significant improvement across most attempts (
conclusionUnlike previous studies, our work has shown the implementation and effectiveness of federated learning processes beyond simulation. Additionally, we have identified different federated learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.
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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.