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

Abstract

Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We aim to simultaneously predict the risk of three symptoms: severe pain, moderate-severe depression, and poor well-being in order to flag patients who may benefit from pre-emptive early symptom management. This was a retrospective population-based cohort study of adults diagnosed with cancer between 2008 and 2015. We developed and tested an Artificial Neural Network (ANN) model to predict the risk of multiple co-occurring symptoms within 6 months after diagnosis. The ANN model derived from a training cohort was assessed on an independent test cohort for model performance based on sensitivity, specificity, accuracy, AUC, and calibration. The mutually exclusive training and test cohorts consisted of 35,606 and 10,498 patients, respectively. The area under the curve for the risk of experiencing severe pain, moderate-severe depression, and poor well-being were 71%, 73%, and 70%, respectively. Patient characteristics at highest risk of simultaneously experiencing these three symptoms included: those with lung cancer, late stage cancer, existing chronic conditions such as osteoarthritis, mood disorder, hypertension, diabetes, and coronary disease. Patients with over a 40% risk of severe pain also had over a 70% risk of depression, and over a 55% risk of poor well-being. Our ANN model was able to simultaneously predict the risk of pain, depression, and lack of well-being. Accurate prediction of future symptom burden can serve as an early indicator tool so that providers can implement timely interventions for symptom management, ultimately improving cancer care and quality of life.

Keywords: Edmonton Symptom Assessment System; artificial neural network; calibration; co-occurrence; discrimination; model validation; simultaneous prediction; symptom burden.

FIGURE 1

Visualization of a 10‐3‐3 neural network: 1 input layer consisting of 10 nodes (x1–x10), 1 hidden layer consisting of 3 nodes (h1–h3), and 1 output layer consisting of 3 nodes (o1–o3). The grey lines represent the connections/weights that need to be estimated. Note the network described herein was 39‐3‐3

FIGURE 2

Calibration plot for each symptom under the ANN risk prediction model (on the test cohort)

FIGURE 3

3‐Dimensional scatter plot of predicted risk for each symptom from ANN model (on the test cohort)