Latest revision as of 10:17, 15 February 2021

Abstract

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Three common and inter-related symptoms in cancer patients are depression, anxiety, and sleep disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression (SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to predict the severity of the aforementioned symptoms between two different time points during a cycle of chemotherapy (CTX). Our results demonstrate that these two methods produced equivalent results for all three symptoms. These types of predictive models can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions.

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• http://dx.doi.org/10.1371/journal.pone.0208808 under the license http://creativecommons.org/licenses/by/4.0

• http://epubs.surrey.ac.uk/849947 under the license http://creativecommons.org/licenses/by/4.0

• http://europepmc.org/articles/PMC6312306

• http://hdl.handle.net/10044/1/83131

• https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable under the license http://creativecommons.org/licenses/by/4.0/

• https://strathprints.strath.ac.uk/66580/1/Papachristou_etal_POS2018_Learning_from_data_to_predict_future_symptoms_of_oncology.pdf under the license cc-by

• https://zenodo.org/record/3368254 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.plos.org/10.1371/journal.pone.0208808,

http://dx.doi.org/10.1371/journal.pone.0208808 under the license http://creativecommons.org/licenses/by/4.0/legalcode

• https://doi.org/10.1371/journal.pone.0208808,

https://doaj.org/toc/1932-6203 under the license http://creativecommons.org/licenses/by/4.0/

• https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208808,

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312306,

http://epubs.surrey.ac.uk/849947,

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0208808&type=printable,

https://strathprints.strath.ac.uk/66580,

http://ui.adsabs.harvard.edu/abs/2018PLoSO..1308808P/abstract,

https://nyuscholars.nyu.edu/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient,

https://pureportal.strath.ac.uk/en/publications/learning-from-data-to-predict-future-symptoms-of-oncology-patient,

http://europepmc.org/articles/PMC6312306,

https://dx.plos.org/10.1371/journal.pone.0208808,

https://academic.microsoft.com/#/detail/2903477703