论文标题
它有多少伤害：一个慢性疼痛评分评估的深度学习框架
How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment
论文作者
论文摘要
慢性疼痛被定义为疼痛，持续或复发超过3到6个月，通常是在最初导致疼痛的受伤或疾病愈合后很长时间。慢性疼痛评估的“黄金标准”仍然通过生物心理社会访谈进行自我报告和临床评估，因为没有设备可以衡量它。测量疼痛的装置不仅对临床评估有用，而且有可能用作生物反馈装置，从而减轻疼痛。在本文中，我们提出了一个端到端的深度学习框架，用于慢性疼痛评分评估。我们的深度学习框架将长时间的数据样本分为较短的序列，并使用共识预测来对结果进行分类。我们在两个原型疼痛计的迭代中收集的两个慢性疼痛评分数据集评估了框架的性能，我们已经开发出来帮助慢性疼痛受试者更好地了解其健康状况。
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.
