Learning from Data to Predict Future Symptoms of Oncology Patients
2018
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.
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