Temporal Matrix Completion with Locally Linear Latent Factors for Medical Applications

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-ba...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2016-10
Main Authors: Frodo Kin Sun Chan, Ma, Andy J, Yuen, Pong C, Terry Cheuk-Fung Yip, Yee-Kit Tse, Vincent Wai-Sun Wong, Grace Lai-Hung Wong
Format: Article
Language:English
Subjects:
Algorithms
Data correlation
Mathematical analysis
Matrix methods
Missing data
Tensors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422