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 mut

SUBMITTER: Xuyi W 

PROVIDER: S-EPMC7897969 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature