TY - JOUR
T1 - Learning from data to predict future symptoms of oncology patients
AU - Papachristou, Nikolaos
AU - Puschmann, Daniel
AU - Barnaghi, Payam
AU - Cooper, Bruce
AU - Hu, Xiao
AU - Maguire, Roma
AU - Apostolidis, Kathi
AU - Conley, Yvette P.
AU - Hammer, Marilyn
AU - Katsaragakis, Stylianos
AU - Kober, Kord M.
AU - Levine, Jon D.
AU - McCann, Lisa
AU - Patiraki, Elisabeth
AU - Furlong, Eileen P.
AU - Fox, Patricia A.
AU - Paul, Steven M.
AU - Ream, Emma
AU - Wright, Fay
AU - Miaskowski, Christine
N1 - Funding Information:
Part of this study was funded by the National Cancer Institute (CA134900). This project received funding, also, from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement No 602289. Finally, this project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732679. In accordance with the National Institutes of Health Data Sharing Plan for this study, data files for this study will be made available following the procedures outlined in our Data Sharing Plan. To request access, please contact Judy.Mastick@ucsf.edu.
Publisher Copyright:
© 2018 Papachristou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2018/12
Y1 - 2018/12
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0208808
DO - 10.1371/journal.pone.0208808
M3 - Article
C2 - 30596658
AN - SCOPUS:85059291212
VL - 13
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 12
M1 - e0208808
ER -