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Learning from data to predict future symptoms of oncology patients
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...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312306/ https://www.ncbi.nlm.nih.gov/pubmed/30596658 http://dx.doi.org/10.1371/journal.pone.0208808 |
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author | Papachristou, Nikolaos Puschmann, Daniel Barnaghi, Payam Cooper, Bruce Hu, Xiao Maguire, Roma Apostolidis, Kathi P. Conley, Yvette Hammer, Marilyn Katsaragakis, Stylianos M. Kober, Kord D. Levine, Jon McCann, Lisa Patiraki, Elisabeth P. Furlong, Eileen A. Fox, Patricia M. Paul, Steven Ream, Emma Wright, Fay Miaskowski, Christine |
author_facet | Papachristou, Nikolaos Puschmann, Daniel Barnaghi, Payam Cooper, Bruce Hu, Xiao Maguire, Roma Apostolidis, Kathi P. Conley, Yvette Hammer, Marilyn Katsaragakis, Stylianos M. Kober, Kord D. Levine, Jon McCann, Lisa Patiraki, Elisabeth P. Furlong, Eileen A. Fox, Patricia M. Paul, Steven Ream, Emma Wright, Fay Miaskowski, Christine |
author_sort | Papachristou, Nikolaos |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6312306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-63123062019-01-08 Learning from data to predict future symptoms of oncology patients Papachristou, Nikolaos Puschmann, Daniel Barnaghi, Payam Cooper, Bruce Hu, Xiao Maguire, Roma Apostolidis, Kathi P. Conley, Yvette Hammer, Marilyn Katsaragakis, Stylianos M. Kober, Kord D. Levine, Jon McCann, Lisa Patiraki, Elisabeth P. Furlong, Eileen A. Fox, Patricia M. Paul, Steven Ream, Emma Wright, Fay Miaskowski, Christine PLoS One Research Article 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. Public Library of Science 2018-12-31 /pmc/articles/PMC6312306/ /pubmed/30596658 http://dx.doi.org/10.1371/journal.pone.0208808 Text en © 2018 Papachristou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Papachristou, Nikolaos Puschmann, Daniel Barnaghi, Payam Cooper, Bruce Hu, Xiao Maguire, Roma Apostolidis, Kathi P. Conley, Yvette Hammer, Marilyn Katsaragakis, Stylianos M. Kober, Kord D. Levine, Jon McCann, Lisa Patiraki, Elisabeth P. Furlong, Eileen A. Fox, Patricia M. Paul, Steven Ream, Emma Wright, Fay Miaskowski, Christine Learning from data to predict future symptoms of oncology patients |
title | Learning from data to predict future symptoms of oncology patients |
title_full | Learning from data to predict future symptoms of oncology patients |
title_fullStr | Learning from data to predict future symptoms of oncology patients |
title_full_unstemmed | Learning from data to predict future symptoms of oncology patients |
title_short | Learning from data to predict future symptoms of oncology patients |
title_sort | learning from data to predict future symptoms of oncology patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6312306/ https://www.ncbi.nlm.nih.gov/pubmed/30596658 http://dx.doi.org/10.1371/journal.pone.0208808 |
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