How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment
This work addresses the need for a device to measure chronic pain, which could aid clinical assessment and serve as a biofeedback tool for pain reduction, though it appears incremental as it builds on existing deep learning methods for a specific application.
The paper tackles the problem of assessing chronic pain scores by proposing an end-to-end deep learning framework that splits long time-course data into shorter sequences and uses Consensus Prediction for classification, evaluated on two datasets from prototype Pain Meters.
Chronic pain is defined as pain that lasts or recurs for more than 3 to 6 months, often long after the injury or illness that initially caused the pain has healed. The "gold standard" for chronic pain assessment remains self report and clinical assessment via a biopsychosocial interview, since there has been no device that can measure it. A device to measure pain would be useful not only for clinical assessment, but potentially also as a biofeedback device leading to pain reduction. In this paper we propose an end-to-end deep learning framework for chronic pain score assessment. Our deep learning framework splits the long time-course data samples into shorter sequences, and uses Consensus Prediction to classify the results. We evaluate the performance of our framework on two chronic pain score datasets collected from two iterations of prototype Pain Meters that we have developed to help chronic pain subjects better understand their health condition.