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

Abstract

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.

Wearable sensors in patient acuity assessment in critical care

“…These studies have underscored the potential of machine learning to accelerate workflow, enhance performance, and improve the accessibility of artificial intelligence in clinical research. Moreover, the work by Wang et al ( 14 ) has highlighted the opportunity presented by EHR data for patient similarity assessment and personalized medicine through machine learning. Advanced algorithms using deep learning techniques have proven superior to conventional bedside severity evaluations in predicting in-hospital deaths, an indirect measure of immediate patient acuity.…”