# Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions

arXiv: https://arxiv.org/abs/2209.00322 · HTML: https://mlsanity.com/paper/2209.00322/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 123 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Generalized Low Rank Models](https://mlsanity.com/paper/1410.0342/) (2014)
- [Federated Tensor Factorization for Computational Phenotyping](https://mlsanity.com/paper/1704.03141/) (2017)
- [COPA: Constrained PARAFAC2 for Sparse & Large Datasets](https://mlsanity.com/paper/1803.04572/) (2018)
- [Natural Language Processing for EHR-Based Computational Phenotyping](https://mlsanity.com/paper/1806.04820/) (2018)
- [Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness](https://mlsanity.com/paper/1404.4412/) (2014)
- [SPARTan: Scalable PARAFAC2 for Large & Sparse Data](https://mlsanity.com/paper/1703.04219/) (2017)
- [An AO-ADMM approach to constraining PARAFAC2 on all modes](https://mlsanity.com/paper/2110.01278/) (2021)
- [Nonnegative PARAFAC2: a flexible coupling approach](https://mlsanity.com/paper/1802.05035/) (2018)
- [TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records](https://mlsanity.com/paper/1911.05843/) (2019)
- [A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations with Linear Couplings](https://mlsanity.com/paper/2007.09605/) (2020)
- [Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis](https://mlsanity.com/paper/1908.09888/) (2019)
- [Towards Flexible Sparsity-Aware Modeling: Automatic Tensor Rank Learning Using The Generalized Hyperbolic Prior](https://mlsanity.com/paper/2009.02472/) (2020)
- [Learning Multimorbidity Patterns from Electronic Health Records Using Non-negative Matrix Factorisation](https://mlsanity.com/paper/1907.08577/) (2019)
- [Automatic Unsupervised Tensor Mining with Quality Assessment](https://mlsanity.com/paper/1503.03355/) (2015)
- [Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records](https://mlsanity.com/paper/2011.06301/) (2020)
- [Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization](https://mlsanity.com/paper/1608.00704/) (2016)
- [SUSTain: Scalable Unsupervised Scoring for Tensors and its Application to Phenotyping](https://mlsanity.com/paper/1803.05473/) (2018)
- [Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning](https://mlsanity.com/paper/1909.11913/) (2019)

## Built on by (most cited first, 4 of 4)

- [SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping](https://mlsanity.com/paper/2310.01201/) (2023)
- [Toward Temporal Causal Representation Learning with Tensor Decomposition](https://mlsanity.com/paper/2507.14126/) (2025)
- [Data-Driven Discovery of Feature Groups in Clinical Time Series](https://mlsanity.com/paper/2511.08260/) (2025)
- [Supervised Coupled Matrix-Tensor Factorization (SCMTF) for Computational Phenotyping of Patient Reported Outcomes in Ulcerative Colitis](https://mlsanity.com/paper/2506.20065/) (2025)

## Abstract (arXiv)

Computational phenotyping allows for unsupervised discovery of subgroups of patients as well as corresponding co-occurring medical conditions from electronic health records (EHR). Typically, EHR data contains demographic information, diagnoses and laboratory results. Discovering (novel) phenotypes has the potential to be of prognostic and therapeutic value. Providing medical practitioners with transparent and interpretable results is an important requirement and an essential part for advancing precision medicine. Low-rank data approximation methods such as matrix (e.g., non-negative matrix factorization) and tensor decompositions (e.g., CANDECOMP/PARAFAC) have demonstrated that they can provide such transparent and interpretable insights. Recent developments have adapted low-rank data approximation methods by incorporating different constraints and regularizations that facilitate interpretability further. In addition, they offer solutions for common challenges within EHR data such as high dimensionality, data sparsity and incompleteness. Especially extracting temporal phenotypes from longitudinal EHR has received much attention in recent years. In this paper, we provide a comprehensive review of low-rank approximation-based approaches for computational phenotyping. The existing literature is categorized into temporal vs. static phenotyping approaches based on matrix vs. tensor decompositions. Furthermore, we outline different approaches for the validation of phenotypes, i.e., the assessment of clinical significance.