DETECT: Data-Driven Evaluation of Treatments Enabled by Classification Transformers
This provides an objective tool for physicians to assess treatment success in chronic pain, though it appears incremental as it builds on existing classification transformers and sensor data methods.
The paper tackled the problem of objectively measuring the functional impact of clinical treatments for chronic pain by proposing DETECT, a data-driven framework that compares patient activities before and after treatment using smartphone sensor data, and demonstrated its objectivity and lightweight nature on benchmark datasets.
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.