ArticlePatterns (New York, N.Y.)2024
An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Impact of the updated SOFA-2 score on sepsis diagnosis and prognosis: a retrospective multicenter cohort study.Critical care (London, England) · 2026Observational
- Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.ACM transactions on intelligent systems and technology · 2026Article
- Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.Scientific reports · 2026Article
- Development and validation of random-forest based federated ensemble learning algorithms for delirium prediction using electronic medical records from eleven hospitals in Austria: a retrospective study.BMC medical informatics and decision making · 2026Article
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Triglyceride-glucose index and triglyceride-to-high-density lipoprotein cholesterol ratio as novel predictors of acute kidney injury: an analysis of the UK biobank.Renal failure · 2025Article
- Edge computing with federated learning for early detection of citric acid overdose and adjustment of regional citrate anticoagulation.BMC medical informatics and decision making · 2025Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- FedWeight: mitigating covariate shift of federated learning on electronic health records data through patients re-weighting.NPJ digital medicine · 2025Article
- Risk stratification for early-stage NSCLC progression: a federated learning framework with large-small model synergy.Frontiers in oncology · 2025Article
- Federated systems for automated infection surveillance: a perspective.Antimicrobial resistance and infection control · 2024Review
- An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.Patterns (New York, N.Y.) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Clinical risk prediction with electronic health records (EHR) using machine learning has attracted lots of attentions in recent years, where one of the key challenges is how to protect data privacy. Federated learning (FL) provides a promising framework for building predictive models by leveraging the data from multiple institutions without sharing them. However, data distribution drift across different institutions greatly impacts the performance of FL. In this paper, an adaptive FL framework was proposed to address this challenge. Our framework separated the input features into stable, domain-specific, and conditional-irrelevant parts according to their relationships to clinical outcomes. We evaluate this framework on the tasks of predicting the onset risk of sepsis and acute kidney injury (AKI) for patients in the intensive care unit (ICU) from multiple clinical institutions. The results showed that our framework can achieve better prediction performance compared with existing FL baselines and provide reasonable feature interpretations.
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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.