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.

High symptom burden is associated with impaired quality of life in colorectal cancer patients during chemotherapy:A prospective longitudinal study

“…To be able to offer symptom management at the right times, it is important to understand which symptoms appear during the different chemotherapy phases. [72] Symptoms can vary between different types of cancer and treatment, [72,90,187] and it is important to assess symptoms within the context of specific cancer diagnoses such as CRC. Assessing symptoms with questionnaires can be bothersome for patients, however self-reported questionnaires provide important and unique information from the patients' perspective for identifying those most at risk and their symptoms.…”

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

“…The multivariate regression models on transformed data dropped substantially in performance for the predicted traits berry number and total volume, whereas the svm poly showed the most stable and the best predictive performance for all predicted traits. In contrast, svms are characterized by seeking to minimize the impact of outliers, having the ability for a good generalization [33,62], and have been proven in many different disciplines [56,[63][64][65].…”

Artificial neural networks for simultaneously predicting the risk of multiple co‐occurring symptoms among patients with cancer

“…Such prediction tools can assist providers in risk-profiling patients, identifying those at higher risk of symptom burden, and improving the timing of pre-emptive and personalized symptom management interventions. 20 The ANN model developed in this paper was able to predict the risk of experiencing three co-occurring symptom outcomes: pain, depression and lack of well-being among patients diagnosed with cancer. The model also identified patient characteristics at highest risk of simultaneously experiencing these three symptoms.…”

“…Prior work has used machine learning techniques such as support vector regression and nonlinear canonical correlation analysis to predict the severity of multiple co-occurring symptoms during a cycle of chemotherapy, however, these models were developed and tested with a relatively small sample of cancer patients. 20 This paper has numerous strengths. To our knowledge, it is the first study to simultaneously predict severity of multiple symptoms under an ANN framework using a population-based cohort of patients with cancer.…”

“…Although their model’s level of discrimination was similar compared to our ANN, symptom correlation was ignored and each symptom was predicted independently using separate regressions. Prior work has used machine learning techniques such as support vector regression and nonlinear canonical correlation analysis to predict the severity of multiple co‐occurring symptoms during a cycle of chemotherapy, however, these models were developed and tested with a relatively small sample of cancer patients 20 …”