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. Attached Files Name Description MIMEType Size Downloads Title Multiple adverse effects prediction in longitudinal cancer treatment Author(s) Li, Cheng Gupta, Sunil orcid.org/0000-0002-3308-1930 Rana, Santu orcid.org/0000-0003-2247-850X Nguyen, Vu 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 Document type: Conference Paper Collections: Faculty of Health Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 01 May 2017, 13:08:29 EST Mon, 01 May 2017, 13:09:01 EST Tue, 02 May 2017, 05:00:27 EST Tue, 02 May 2017, 08:20:24 EST Sat, 27 May 2017, 02:32:17 EST Mon, 29 May 2017, 13:52:52 EST Tue, 30 May 2017, 06:06:46 EST Wed, 31 May 2017, 12:16:23 EST Wed, 31 May 2017, 12:26:42 EST Wed, 31 May 2017, 12:28:23 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 41 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 13:52:52 EST

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