ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2017
Federated Tensor Factorization for Computational Phenotyping.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 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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- Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.Computational and structural biotechnology journal · 2024Review
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.JMIR formative research · 2024Article
- FedFSA: Hybrid and federated framework for functional status ascertainment across institutions.Journal of biomedical informatics · 2024Article
- Federated Node Classification over Distributed Ego-Networks with Secure Contrastive Embedding Sharing.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis.Health data science · 2024Article
- Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- A deep learning method to detect opioid prescription and opioid use disorder from electronic health records.International journal of medical informatics · 2023Article
- Data-Driven Phenotyping of Alzheimer's Disease under Epigenetic Conditions Using Partial Volume Correction of PET Studies and Manifold Learning.Biomedicines · 2023Article
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
- A Comprehensive Survey on Federated Learning Techniques for Healthcare Informatics.Computational intelligence and neuroscience · 2023Review
- Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.Cluster computing · 2022Article
- Communication Efficient Tensor Factorization for Decentralized Healthcare Networks.Proceedings. IEEE International Conference on Data Mining · 2021Article
- Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
- Federated Learning for Healthcare Informatics.Journal of healthcare informatics research · 2021Article
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4 authors.
Funding
Abstract
Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.
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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.