ReviewAdvances in kidney disease and health2023
Federated Learning in Health care Using Structured Medical Data.
Review in Advances in kidney disease and health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 59 citations in OpenAlex.
- Artificial Intelligence and Genomic Data Analysis: New Frontiers in Precision Medicine.International journal of molecular sciences · 2026Review
- Risk factors and predictive models for perioperative acute kidney injury in children: a narrative review.Translational pediatrics · 2026Review
- Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN.Scientific reports · 2026Article
- Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors.Bioengineering (Basel, Switzerland) · 2026Article
- Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.Frontiers in digital health · 2026Review
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- Guidance for Fecal Microbiota Transplantation Trials in Ulcerative Colitis: The Second ROME Consensus Conference.Inflammatory bowel diseases · 2025Article
- The PRO-AGE Tool and Its Association With Post Discharge Outcomes in Older Adults Admitted From the Emergency Department.Journal of the American Geriatrics Society · 2025Observational
- EnDuSecFed: an ensemble approach for privacy preserving Federated Learning with dual-security framework for sustainable healthcare.Frontiers in big data · 2025Article
- The development and use of data warehousing in clinical settings: a scoping review.Frontiers in digital health · 2025Review
- Towards generalizable Federated Learning in medical imaging: A real-world case study on mammography data.Computational and structural biotechnology journal · 2025Article
- Ensemble machine learning framework for predicting maternal health risk during pregnancy.Scientific reports · 2024Article
- Consumer Health Informatics to Advance Precision Prevention.Yearbook of medical informatics · 2024Review
- Assessing the Value of Provider-Facing Digital Health Technologies Used in Chronic Disease Management: Toward a Value Framework Based on Multistakeholder Perceptions.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Review
- Current state of data stewardship tools in life science.Frontiers in big data · 2024Review
- Federated machine learning for predicting acute kidney injury in critically ill patients: a multicenter study in Taiwan.Health information science and systems · 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
2 authors at 1 institution in 1 country.
Funding
Abstract
The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.
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.