Multiple adverse effects prediction in longitudinal cancer treatment

Published

Conference Paper

© 2016 IEEE. Adverse effects, such as voice change and fatigue, are prevalent in cancer treatment duration. These adverse effects have been significant burden for patients physically and emotionally. Predicting multiple adverse effects becomes important for patients and oncologists. In this paper, we formulate the prediction of multiple adverse effects in cancer treatment as a longitudinal multiple-output regression problem. The correlated multiple outputs are first decoupled to uncorrelated ones in a new output space. We then propose a comprehensive framework to capture the empirical loss between the predicted value and the ground truth in the transformed space and the temporal smoothness at neighboring prediction points. Experiments were performed on one synthetic data and two real-world datasets including radiotherapy and chemotherapy treatments. Results in terms of root mean square errors (RMSE) and R-value show that our proposed approach is promising for the longitudinal multiple-output regression problem.

Full Text

Duke Authors

Cited Authors

  • Li, C; Gupta, S; Rana, S; Nguyen, V; Venkatesh, S; Ashley, D; Livingston, T

Published Date

  • January 1, 2016

Published In

Volume / Issue

  • 0 /

Start / End Page

  • 3156 - 3161

International Standard Serial Number (ISSN)

  • 1051-4651

International Standard Book Number 13 (ISBN-13)

  • 9781509048472

Digital Object Identifier (DOI)

  • 10.1109/ICPR.2016.7900120

Citation Source

  • Scopus