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Multiple adverse effects prediction in longitudinal cancer treatment
Version 2 2023-06-07, 01:55
Version 1 2017-05-01, 13:08
conference contribution
posted on 2023-06-07, 01:55 authored by C Li, S Gupta, S Rana, TV Nguyen, S Venkatesh, D Ashley, P LivingstonAdverse 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.
History
Pagination
3156-3161Location
Cancun, MexicoPublisher DOI
Start date
2016-12-04End date
2016-12-08ISBN-13
9781509048472Language
engPublication classification
E Conference publication, E1 Full written paper - refereedCopyright notice
2016, IEEETitle of proceedings
ICPR 2016: Proceedings of the 23rd International Conference on Pattern RecognitionEvent
Pattern Recognition. International Conference (23rd : 2016 : Cancun, Mexico)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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No categories selectedKeywords
adverse effectscancer treatmentlongitudinal predictionmultiple-output regression080109 Pattern Recognition and Data MiningFaculty of Science Engineering and Built EnvironmentSchool of Information TechnologyCentre for Pattern Recognition and Data AnalyticsPattern Recognition and Data AnalyticsFaculty of Health4605 Data management and data science
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