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PLoS One. 2018 Dec 31;13(12):e0208808. doi: 10.1371/journal.pone.0208808. eCollection 2018.

Learning from data to predict future symptoms of oncology patients.

Author information

1
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, United Kingdom.
2
University of California, San Francisco, United States of America.
3
University of Strathclyde, Glasgow, Scotland.
4
European Cancer Patient Coalition, Brussels, Belgium.
5
School of Nursing, University of Pittsburgh, Pittsburgh, United States of America.
6
Department of Nursing, Mount Sinai Medical Center, New York, United States of America.
7
Faculty of Nursing, University of Peloponnese, Sparti, Greece.
8
National and Kapodistrian University of Athens, Athens, Greece.
9
UCD School of Nursing, Midwifery and Health Systems, Dublin, Ireland.
10
School of Nursing, Yale University, New Haven, United States of America.

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.

PMID:
30596658
PMCID:
PMC6312306
DOI:
10.1371/journal.pone.0208808
[Indexed for MEDLINE]
Free PMC Article

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