ArticleProceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management2019
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis.
Article in Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2024Article
- Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.JMIR formative research · 2024Article
- Differential privacy in collaborative filtering recommender systems: a review.Frontiers in big data · 2023Review
- Federated Learning in Health care Using Structured Medical Data.Advances in kidney disease and health · 2023Review
- Communication Efficient Tensor Factorization for Decentralized Healthcare Networks.Proceedings. IEEE International Conference on Data Mining · 2021Article
- Recent Developments in Privacy-Preserving Mining of Clinical Data.ACM/IMS transactions on data science · 2021Article
- Differential privacy in health research: A scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
- Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meaningful clinical concepts. While distributing the tensor factorization tasks to local sites can avoid direct data sharing, it still requires the exchange of intermediary results which could reveal sensitive patient information. Therefore, the challenge is how to jointly decompose the tensor under rigorous and principled privacy constraints, while still support the model's interpretability. We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR. It embeds advanced privacy-preserving mechanisms with collaborative learning. Hospitals can keep their EHR database private but also collaboratively learn meaningful clinical concepts by sharing differentially private intermediary results. Moreover, DPFact solves the heterogeneous patient population using a structured sparsity term. In our framework, each hospital decomposes its local tensors and sends the updated intermediary results with output perturbation every several iterations to a semi-trusted server which generates the phenotypes. The evaluation on both real-world and synthetic datasets demonstrated that under strict privacy constraints, our method is more accurate and communication-efficient than state-of-the-art baseline methods.
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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.