[2511.07213] DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers

Computer Science > Machine Learning arXiv:2511.07213 (cs) [Submitted on 10 Nov 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers Authors:Yuanheng Mao, Lillian Yang, Stephen Yang, Ethan Shao, Zihan Li View a PDF of the paper titled DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers, by Yuanheng Mao and 4 other authors View PDF HTML (experimental) Abstract: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 pers...