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Learning from Data to Predict Future Symptoms of Oncology Patients

Papachristou, Nikolaos, Puschmann, Daniel, Barnaghi, Payam, Cooper, Bruce, Hu, Xiao, Maguire, Roma, Apostolidis, Kathi, Conley, Yvette P, Hammer, Marilyn, Katsaragakis, Stylianos , Kober, Kord M, Levine, Jon D, McCann, Lisa, Patiraki, Elisabeth, Furlong, Eileen P, Fox, Patricia A, Paul, Steven M, Ream, Emma, Wright, Fay and Miaskowski, Christine (2018) Learning from Data to Predict Future Symptoms of Oncology Patients PLoS ONE.

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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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Cooper, Bruce
Hu, Xiao
Apostolidis, Kathi
Conley, Yvette P
Hammer, Marilyn
Katsaragakis, Stylianos
Kober, Kord M
Levine, Jon D
McCann, Lisa
Patiraki, Elisabeth
Furlong, Eileen P
Fox, Patricia A
Paul, Steven M
Wright, Fay
Miaskowski, Christine
Date : 2018
Copyright Disclaimer : Copyright 2018 The Author(s)
Depositing User : Melanie Hughes
Date Deposited : 28 Nov 2018 10:58
Last Modified : 28 Nov 2018 10:58

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