How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment

TL;DR

This paper introduces a deep learning framework that analyzes long-term data to assess chronic pain scores, aiming to improve clinical evaluation and biofeedback tools.

Contribution

It presents a novel end-to-end deep learning approach with consensus prediction for chronic pain assessment using long time-course data.

Findings

Effective classification of pain scores on two datasets

Improved accuracy over traditional methods

Potential for real-time pain monitoring

Abstract

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.