# SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding

arXiv: https://arxiv.org/abs/2202.01427 · HTML: https://mlsanity.com/paper/2202.01427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 56 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review](https://mlsanity.com/paper/2010.02809/) (2020)
- [Imputation and low-rank estimation with Missing Not At Random data](https://mlsanity.com/paper/1812.11409/) (2018)
- [Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding](https://mlsanity.com/paper/1902.03376/) (2019)
- [Classification with Low Rank and Missing Data](https://mlsanity.com/paper/1501.03273/) (2015)
- [A Survey on Matrix Completion: Perspective of Signal Processing](https://mlsanity.com/paper/1901.10885/) (2019)
- [Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations](https://mlsanity.com/paper/1810.03523/) (2018)

## Abstract (arXiv)

Patient similarity assessment (PSA) is pivotal to evidence-based and personalized medicine, enabled by analyzing the increasingly available electronic health records (EHRs). However, machine learning approaches for PSA has to deal with inherent data deficiencies of EHRs, namely missing values, noise, and small sample sizes. In this work, an end-to-end discriminative learning framework, called SparGE, is proposed to address these data challenges of EHR for PSA. SparGE measures similarity by jointly sparse coding and graph embedding. First, we use low-rank constrained sparse coding to identify and calculate weight for similar patients, while denoising against missing values. Then, graph embedding on sparse representations is adopted to measure the similarity between patient pairs via preserving local relationships defined by distances. Finally, a global cost function is constructed to optimize related parameters. Experimental results on two private and public real-world healthcare datasets, namely SingHEART and MIMIC-III, show that the proposed SparGE significantly outperforms other machine learning patient similarity methods.