ArticleJMIR medical informatics2018
Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis.
Article in JMIR medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.Journal of biomedical informatics · 2021Pooled it
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- FedECA: federated external control arms for causal inference with time-to-event data in distributed settings.Nature communications · 2025Article
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- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Privacy-preserving decentralized learning methods for biomedical applications.Computational and structural biotechnology journal · 2024Review
- FLCMC: Federated Learning Approach for Chinese Medicinal Text Classification.Entropy (Basel, Switzerland) · 2024Article
- A federated learning architecture for secure and private neuroimaging analysis.Patterns (New York, N.Y.) · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- A decentralized federated learning-based cancer survival prediction method with privacy protection.Heliyon · 2024Article
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.Journal of biomedical informatics · 2024Article
- Federated clustered multi-domain learning for health monitoring.Scientific reports · 2024Article
- TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment.Journal of multidisciplinary healthcare · 2024Article
- Predicting anticancer drug sensitivity on distributed data sources using federated deep learning.Heliyon · 2023Article
- A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.Computational intelligence and neuroscience · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThere is an urgent need for the development of global analytic frameworks that can perform analyses in a privacy-preserving federated environment across multiple institutions without privacy leakage. A few studies on the topic of federated medical analysis have been conducted recently with the focus on several algorithms. However, none of them have solved similar patient matching, which is useful for applications such as cohort construction for cross-institution observational studies, disease surveillance, and clinical trials recruitment.
objectiveThe aim of this study was to present a privacy-preserving platform in a federated setting for patient similarity learning across institutions. Without sharing patient-level information, our model can find similar patients from one hospital to another.
methodsWe proposed a federated patient hashing framework and developed a novel algorithm to learn context-specific hash codes to represent patients across institutions. The similarities between patients can be efficiently computed using the resulting hash codes of corresponding patients. To avoid security attack from reverse engineering on the model, we applied homomorphic encryption to patient similarity search in a federated setting.
resultsWe used sequential medical events extracted from the Multiparameter Intelligent Monitoring in Intensive Care-III database to evaluate the proposed algorithm in predicting the incidence of five diseases independently. Our algorithm achieved averaged area under the curves of 0.9154 and 0.8012 with balanced and imbalanced data, respectively, in κ-nearest neighbor with κ=3. We also confirmed privacy preservation in similarity search by using homomorphic encryption.
conclusionsThe proposed algorithm can help search similar patients across institutions effectively to support federated data analysis in a privacy-preserving manner.
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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.