用手机数据客观评估慢性疼痛治疗效果。
DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers
- 基于分类变压器分析患者治疗前后日常活动变化。
- 在公开数据集与模拟数据上验证了方法的客观性与轻量性。
- 适合临床医生用于辅助决策，提升个性化诊疗水平。
慢性疼痛是全球性健康挑战，影响数百万人，因此医生需要可靠、客观的方法来评估治疗对功能的影响。传统方法如数字评分量表虽个性化且易用，但因依赖自述而具有主观性。本文提出 DETECT（基于分类变压器的数据驱动治疗评估框架），通过比较患者治疗前后的日常生活活动来评估治疗成效。我们在公开基准数据集和智能手机传感器生成的模拟患者数据上应用 DETECT。结果表明，DETECT 具备客观性与轻量化特性，为临床决策提供了重要且新颖的贡献。结合或独立使用该方法，医生可更准确理解治疗影响，推动更加个性化和响应式的患者照护。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。