Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications

TL;DR

This paper introduces a locally linear latent factor model for temporal matrix completion, specifically designed for medical data with irregular time intervals, outperforming tensor-based methods in missing data imputation.

Contribution

The paper proposes a novel matrix decomposition approach with locally linear constraints to improve missing data imputation in irregular temporal medical records.

Findings

Achieves superior performance over existing methods on medical datasets.

Effectively handles long time intervals with low data correlation.

Demonstrates robustness in real-world medical data imputation.

Abstract

Regular medical records are useful for medical practitioners to analyze and monitor patient health status especially for those with chronic disease, but such records are usually incomplete due to unpunctuality and absence of patients. In order to resolve the missing data problem over time, tensor-based model is suggested for missing data imputation in recent papers because this approach makes use of low rank tensor assumption for highly correlated data. However, when the time intervals between records are long, the data correlation is not high along temporal direction and such assumption is not valid. To address this problem, we propose to decompose a matrix with missing data into its latent factors. Then, the locally linear constraint is imposed on these factors for matrix completion in this paper. By using a publicly available dataset and two medical datasets collected from hospital, experimental results show that the proposed algorithm achieves the best performance by comparing with the existing methods.