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

TL;DR

DETECT is a novel, lightweight, data-driven framework that objectively evaluates treatment success in chronic pain patients by analyzing activity data from smartphone sensors, improving clinical decision-making.

Contribution

This paper introduces DETECT, a new framework that uses classification transformers and sensor data to objectively assess treatment outcomes, advancing beyond subjective self-report methods.

Findings

DETECT effectively distinguishes pre- and post-treatment activity patterns.

The framework performs well on benchmark and simulated datasets.

DETECT enhances clinical understanding of treatment impacts.

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 personalized and responsive patient care.