Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
File(s)
Working paper
Author(s)
Type
Working Paper
Abstract
In this work, a novel approach is proposed for joint analysis of high
dimensional time-resolved cardiac motion features obtained from segmented
cardiac MRI and low dimensional clinical risk factors to improve survival
prediction in heart failure. Different methods are evaluated to find the
optimal way to insert conventional covariates into deep prediction networks.
Correlation analysis between autoencoder latent codes and covariate features is
used to examine how these predictors interact. We believe that similar
approaches could also be used to introduce knowledge of genetic variants to
such survival networks to improve outcome prediction by jointly analysing
cardiac motion traits with inheritable risk factors.
dimensional time-resolved cardiac motion features obtained from segmented
cardiac MRI and low dimensional clinical risk factors to improve survival
prediction in heart failure. Different methods are evaluated to find the
optimal way to insert conventional covariates into deep prediction networks.
Correlation analysis between autoencoder latent codes and covariate features is
used to examine how these predictors interact. We believe that similar
approaches could also be used to introduce knowledge of genetic variants to
such survival networks to improve outcome prediction by jointly analysing
cardiac motion traits with inheritable risk factors.
Date Issued
2019-10-07
Citation
2019
Publisher
arXiv
Copyright Statement
© 2019 The Author(s)
Identifier
http://arxiv.org/abs/1910.02951v1
Subjects
q-bio.QM
q-bio.QM
cs.LG
eess.IV
stat.ML
Notes
4 pages, 2 figures
Publication Status
Published