Learning from data to predict future symptoms of oncology patients
Creators
- Papachristou, Nikolaos1
- Puschmann, Daniel1
- Barnaghi, Payam1
- Cooper, Bruce2
- Hu, Xiao2
- Maguire, Roma3
- Apostolidis, Kathi4
- Conley, Yvette P.5
- Hammer, Marilyn6
- Katsaragakis, Stylianos7
- Kober, Kord M.2
- Levine, Jon D.2
- McCann, Lisa3
- Patiraki,, Elisabeth8
- Furlong, Eileen P.9
- Fox, Patricia A.9
- Paul, Steven M.2
- Ream, Emma1
- Wright, Fay10
- Miaskowski, Christine2
- 1. University of Surrey
- 2. University of California
- 3. University of Strathclyde
- 4. European Cancer Patient Coalition
- 5. University of Pittsburgh
- 6. Mount Sinai Medical Center
- 7. University of Peloponnese
- 8. National and Kapodistrian University of Athens
- 9. UCD School of Nursing, Midwifery and Health Systems
- 10. Yale University
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.
Files
Papachristou_etal_PLoSONE2018_Learning_from_data_to_predict_future_symptoms_of_oncology.pdf
Files
(1.8 MB)
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Additional details
Funding
- ESMART – Randomised controlled trial to evaluate electronic Symptom Management using the Advanced Symptom Management System (ASyMS) Remote Technology for patients with cancers 602289
- European Commission
- ACTIVAGE – ACTivating InnoVative IoT smart living environments for AGEing well 732679
- European Commission