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Multiple adverse effects prediction in longitudinal cancer treatment

Li, Cheng, Gupta, Sunil, Rana, Santu, Nguyen, Vu, Venkatesh, Svetha, Ashley, David and Livingston, Trish 2016, Multiple adverse effects prediction in longitudinal cancer treatment, in ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, IEEE, Piscataway, N.J., pp. 3156-3161, doi: 10.0.4.85/ICPR.2016.7900120.

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Title Multiple adverse effects prediction in longitudinal cancer treatment
Author(s) Li, Cheng
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0002-3308-1930
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0003-2247-850X
Nguyen, Vu
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Ashley, David
Livingston, Trish
Conference name Pattern Recognition. International Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 4-8 Dec. 2016
Title of proceedings ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition
Publication date 2016
Conference series Pattern Recognition International Conference
Start page 3156
End page 3161
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) adverse effects
cancer treatment
multiple-output regression
longitudinal prediction
Summary 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 realworld 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 multipleoutput regression problem.
ISBN 9781509048472
Language eng
DOI 10.0.4.85/ICPR.2016.7900120
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094577

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