DETECT：基于分类Transformer的数据驱动治疗效果评估框架 (DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers)

from arxiv, 5 pages, 4 figures, 2 tables, presented and awarded Best Paper Runner-Up at the IEEE ICDM 2025 UGHS Symposium, and publication with proceedings forthcoming

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：慢性疼痛是影响数百万人的全球性健康挑战，这使得医生需要可靠且客观的方法来评估临床治疗对功能状态的影响。传统方法如数字评分量表虽具有个性化、易用性等优点，但其基于自我报告的特性导致结果具有主观性。为此，本文提出DETECT（基于分类Transformer的数据驱动治疗效果评估框架），该数据驱动框架通过比较治疗前后患者的日常生活活动来评估治疗效果。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验证了DETECT框架。实验结果表明，DETECT兼具客观性与轻量化特性，为临床决策提供了重要且新颖的解决方案。通过单独或结合其他自我报告指标使用DETECT，医生能够更准确地评估治疗效果，最终实现更具个性化与响应性的患者护理。