Learning from data to predict future symptoms of oncology patients

Nikolaos Papachristou, Daniel Puschmann, Payam Barnaghi, Bruce Cooper, Xiao Hu, Roma Maguire, Kathi Apostolidis, Yvette P. Conley, Marilyn Hammer, Stylianos Katsaragakis, Kord M. Kober, Jon D. Levine, Lisa McCann, Elisabeth Patiraki, Eileen P. Furlong, Patricia A. Fox, Steven M. Paul, Emma Ream, Fay Wright, Christine Miaskowski

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.
LanguageEnglish
Article numbere0208808
Number of pages17
JournalPLoS ONE
Volume13
Issue number12
DOIs
Publication statusPublished - 31 Dec 2018

Fingerprint

Oncology
signs and symptoms (animals and humans)
learning
Learning
Patient treatment
Chemotherapy
Neural networks
Linear Models
Neoplasms
Sleep
Anxiety
Depression
neoplasms
Efficiency
Drug Therapy
anxiety
sleep
neural networks
Therapeutics
drug therapy

Keywords

  • symptom management
  • cancer treatment research
  • oncology patients
  • chemotherapy

Cite this

Papachristou, N., Puschmann, D., Barnaghi, P., Cooper, B., Hu, X., Maguire, R., ... Miaskowski, C. (2018). Learning from data to predict future symptoms of oncology patients. PLoS ONE, 13(12), [e0208808]. https://doi.org/10.1371/journal.pone.0208808
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 ; Miaskowski, Christine. / Learning from data to predict future symptoms of oncology patients. In: PLoS ONE. 2018 ; Vol. 13, No. 12.
@article{ce8656394e6a4d21bbad91a6e193b3c5,
title = "Learning from data to predict future symptoms of oncology patients",
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.",
keywords = "symptom management, cancer treatment research, oncology patients, chemotherapy",
author = "Nikolaos Papachristou and Daniel Puschmann and Payam Barnaghi and Bruce Cooper and Xiao Hu and Roma Maguire and Kathi Apostolidis and Conley, {Yvette P.} and Marilyn Hammer and Stylianos Katsaragakis and Kober, {Kord M.} and Levine, {Jon D.} and Lisa McCann and Elisabeth Patiraki and Furlong, {Eileen P.} and Fox, {Patricia A.} and Paul, {Steven M.} and Emma Ream and Fay Wright and Christine Miaskowski",
year = "2018",
month = "12",
day = "31",
doi = "10.1371/journal.pone.0208808",
language = "English",
volume = "13",
journal = "PLOS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "12",

}

Papachristou, N, Puschmann, D, Barnaghi, P, Cooper, B, Hu, X, Maguire, R, Apostolidis, K, Conley, YP, Hammer, M, Katsaragakis, S, Kober, KM, Levine, JD, McCann, L, Patiraki, E, Furlong, EP, Fox, PA, Paul, SM, Ream, E, Wright, F & Miaskowski, C 2018, 'Learning from data to predict future symptoms of oncology patients' PLoS ONE, vol. 13, no. 12, e0208808. https://doi.org/10.1371/journal.pone.0208808

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; Miaskowski, Christine.

In: PLoS ONE, Vol. 13, No. 12, e0208808, 31.12.2018.

Research output: Contribution to journalArticle

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

PY - 2018/12/31

Y1 - 2018/12/31

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.

KW - symptom management

KW - cancer treatment research

KW - oncology patients

KW - chemotherapy

UR - https://journals.plos.org/plosone/

U2 - 10.1371/journal.pone.0208808

DO - 10.1371/journal.pone.0208808

M3 - Article

VL - 13

JO - PLOS One

T2 - PLOS One

JF - PLOS One

SN - 1932-6203

IS - 12

M1 - e0208808

ER -

Papachristou N, Puschmann D, Barnaghi P, Cooper B, Hu X, Maguire R et al. Learning from data to predict future symptoms of oncology patients. PLoS ONE. 2018 Dec 31;13(12). e0208808. https://doi.org/10.1371/journal.pone.0208808