ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
A Federated Mining Approach on Predicting Diabetes-Related Complications: Demonstration Using Real-World Clinical Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in the prediction of diabetic peripheral neuropathy: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework.JMIR diabetes · 2026Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- Federated Diabetes Prediction in Canadian Adults Using Real-world Cross-Province Primary Care Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction.Sensors (Basel, Switzerland) · 2023Article
- Reviewing Federated Machine Learning and Its Use in Diseases Prediction.Sensors (Basel, Switzerland) · 2023Review
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chronic diabetes can lead to microvascular complications, including diabetic eye disease, diabetic kidney disease, and diabetic neuropathy. However, the long-term complications often remain undetected at the early stages of diagnosis. Developing a machine learning model to identify the patients at high risk of developing diabetes-related complications can help design better treatment interventions. Building robust machine learning models require large datasets which further requires sharing data among different healthcare systems, hence, involving privacy and confidentiality concerns. The main
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Identifiers
35308968PMC8861723What 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.