Chronic pain is a global health challenge affecting millions of individuals, making it essential for physicians to have reliable and objective methods to measure the functional impact of clinical treatments. Traditionally used methods, like the numeric rating scale, while personalized and easy to use, are subjective due to their self-reported nature. Thus, this paper proposes DETECT (Data-Driven Evaluation of Treatments Enabled by Classification Transformers), a data-driven framework that assesses treatment success by comparing patient activities of daily life before and after treatment. We use DETECT on public benchmark datasets and simulated patient data from smartphone sensors. Our results demonstrate that DETECT is objective yet lightweight, making it a significant and novel contribution to clinical decision-making. By using DETECT, independently or together with other self-reported metrics, physicians can improve their understanding of their treatment impacts, ultimately leading to more personalized and responsive patient care.

翻译：慢性疼痛是全球性的健康挑战，影响着数百万患者，因此医生需要可靠且客观的方法来评估临床治疗对功能的影响。传统方法如数字评分量表虽具有个性化且易于使用的特点，但由于其自我报告的性质而存在主观性。为此，本文提出DETECT（基于分类Transformer的数据驱动治疗评估框架），该框架通过比较患者治疗前后日常生活活动数据来评估治疗成效。我们在公开基准数据集及智能手机传感器模拟的患者数据上应用DETECT。结果表明，DETECT具有客观且轻量化的特点，为临床决策提供了重要且新颖的贡献。通过单独或结合其他自我报告指标使用DETECT，医生能够更准确地理解治疗影响，最终实现更个性化、响应更及时的患者照护。