ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2015
Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics.
Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Integrated analysis for electronic health records with structured and sporadic missingness.Journal of biomedical informatics · 2025Article
- Creating High-Quality Synthetic Health Data: Framework for Model Development and Validation.JMIR formative research · 2024Article
- MASMDDI: multi-layer adaptive soft-mask graph neural network for drug-drug interaction prediction.Frontiers in pharmacology · 2024Article
- MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning for Computational Phenotyping.Proceedings of machine learning research · 2023Article
- CCPE: cell cycle pseudotime estimation for single cell RNA-seq data.Nucleic acids research · 2022Article
- Communication Efficient Tensor Factorization for Decentralized Healthcare Networks.Proceedings. IEEE International Conference on Data Mining · 2021Article
- Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.Patterns (New York, N.Y.) · 2021Article
- Communication Efficient Federated Generalized Tensor Factorization for Collaborative Health Data Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2021Article
- Untangling the complexity of multimorbidity with machine learning.Mechanisms of ageing and development · 2020Review
- Temporal phenotyping for transitional disease progress: An application to epilepsy and Alzheimer's disease.Journal of biomedical informatics · 2020Article
- Inferring multimodal latent topics from electronic health records.Nature communications · 2020Article
- 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
- Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.Journal of biomedical informatics · 2019Article
- Phenotype Inference with Semi-Supervised Mixed Membership Models.Proceedings of machine learning research · 2019Article
- Predicting patient's diagnoses and diagnostic categories from clinical-events in EHR data.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2019Article
- CP Tensor Decomposition with Cannot-Link Intermode Constraints.Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining · 2019Article
- Distributed Tensor Decomposition for Large Scale Health Analytics.Proceedings of the ... International World-Wide Web Conference. International WWW Conference · 2019Article
- The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- COPA: Constrained PARAFAC2 for Sparse & Large Datasets.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2018Article
- Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.Annual review of biomedical data science · 2018Article
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Authors and funding
8 authors.
Funding
Abstract
Computational phenotyping is the process of converting heterogeneous electronic health records (EHRs) into meaningful clinical concepts. Unsupervised phenotyping methods have the potential to leverage a vast amount of labeled EHR data for phenotype discovery. However, existing unsupervised phenotyping methods do not incorporate current medical knowledge and cannot directly handle missing, or noisy data. We propose Rubik, a constrained non-negative tensor factorization and completion method for phenotyping. Rubik incorporates 1) guidance constraints to align with existing medical knowledge, and 2) pairwise constraints for obtaining distinct, non-overlapping phenotypes. Rubik also has built-in tensor completion that can significantly alleviate the impact of noisy and missing data. We utilize the Alternating Direction Method of Multipliers (ADMM) framework to tensor factorization and completion, which can be easily scaled through parallel computing. We evaluate Rubik on two EHR datasets, one of which contains 647,118 records for 7,744 patients from an outpatient clinic, the other of which is a public dataset containing 1,018,614 CMS claims records for 472,645 patients. Our results show that Rubik can discover more meaningful and distinct phenotypes than the baselines. In particular, by using knowledge guidance constraints, Rubik can also discover sub-phenotypes for several major diseases. Rubik also runs around seven times faster than current state-of-the-art tensor methods. Finally, Rubik is scalable to large datasets containing millions of EHR records.
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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.