@article{3056524,
    title = "Learning from data to predict future symptoms of oncology patients",
    author = "Papachristou, N. and Puschmann, D. and Barnaghi, P. and Cooper, B. and Hu, X. and Maguire, R. and Apostolidis, K. and Conley, Y.P. and Hammer, M. and Katsaragakis, S. and Kober, K.M. and Levine, J.D. and McCann, L. and Patiraki, E. and Furlong, E.P. and Fox, P.A. and Paul, S.M. and Ream, E. and Wright, F. and Miaskowski, C.",
    journal = "PLOS ONE",
    year = "2018",
    volume = "13",
    number = "12",
    publisher = "Public Library of Science",
    doi = "10.1371/journal.pone.0208808",
    keywords = "adult;  article;  cancer chemotherapy;  correlation analysis;  high risk patient;  human;  learning;  oncology;  risk assessment;  support vector machine;  anxiety;  artificial neural network;  clinical trial;  depression;  female;  male;  multicenter study;  neoplasm;  psychological model;  psychology;  support vector machine, Anxiety;  Depression;  Female;  Humans;  Male;  Models, Psychological;  Neoplasms;  Neural Networks (Computer);  Support Vector Machine",
    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. © 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."
}