ReviewAnnual review of biomedical data science2024
Centralized and Federated Models for the Analysis of Clinical Data.
Review in Annual review of biomedical data science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Discovering repurposable drugs for Alzheimer's disease and related dementias: target trial emulation using decentralised real-world data.EBioMedicine · 2026Article
- When One Plus One Does Not Equal Two: Embracing the Dynamic and Living Nature of Real-World Evidence.International journal of rheumatic diseases · 2026Article
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Agentic AI and the rise of in silico team science in biomedical research.Nature biotechnology · 2026Review
- Humanizing the pediatric intensive care unit through new technologies and artificial intelligence.Frontiers in health services · 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
- Federated causal discovery in medicine: trends, opportunities, and challenges.Frontiers in digital health · 2026Article
- Big multiple sclerosis data network: novel modelling approaches for real-world data analysis.Journal of neurology · 2025Review
- Developing and Validating Machine Learning-Driven Risk Indices to Predict Patient Dropout During Referral, Evaluation, and Waitlisting for Kidney Transplant.Clinical transplantation · 2025Article
- COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.NPJ digital medicine · 2025Article
- Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.Critical care (London, England) · 2025Review
- Leveraging real-world data for safety signal detection and risk management in pre- and post-market settings.Frontiers in drug safety and regulation · 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
4 authors.
Funding
Abstract
The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a comprehensive understanding of diseases. In this review, we describe two important approaches for the analysis of diverse clinical datasets, namely the centralized model and federated model. We compare and contrast the strengths and weaknesses inherent in each model and present recent progress in methodologies and their associated challenges. Finally, we present an outlook on the opportunities that both models hold for the future analysis of clinical data.
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.