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

Yuanheng Mao, Lillian Yang, Stephen Yang, Ethan Shao, Zihan Li

TL;DR

DETECT introduces a data-driven, transformer-based framework for objectively evaluating chronic pain treatment impact by comparing ADL patterns from smartphone-derived IMU data collected before and after intervention. The approach uses a pre-treatment trained classifier, TES as a quantification of behavioral change, and a threshold derived from NRS-improved patients to indicate significant treatment effects. Experiments on simulated and public benchmark data show DETECT can reveal treatment-related shifts even with limited real-world data, while maintaining high baseline recognition performance on standard datasets. The framework emphasizes accessibility, personalization, and objective assessment, offering a scalable tool to augment traditional self-reported pain measures in clinical decision-making.

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.

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

TL;DR

DETECT introduces a data-driven, transformer-based framework for objectively evaluating chronic pain treatment impact by comparing ADL patterns from smartphone-derived IMU data collected before and after intervention. The approach uses a pre-treatment trained classifier, TES as a quantification of behavioral change, and a threshold derived from NRS-improved patients to indicate significant treatment effects. Experiments on simulated and public benchmark data show DETECT can reveal treatment-related shifts even with limited real-world data, while maintaining high baseline recognition performance on standard datasets. The framework emphasizes accessibility, personalization, and objective assessment, offering a scalable tool to augment traditional self-reported pain measures in clinical decision-making.

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.

Paper Structure

This paper contains 15 sections, 4 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Accessibility
  5. The DETECT Framework
  6. Experimental Results
  7. Data Collection
  8. Data Processing
  9. Model Training
  10. Simulated Pain Data Results
  11. Public Benchmark Data Results
  12. Discussion
  13. Limitations
  14. Future Works
  15. Conclusion

Figures (4)

  • Figure 1: The DETECT Framework compares pre- and post-treatment activity data using a transformer model to evaluate treatment impact, guiding physicians to improve patient care.
  • Figure 2: Graphs displaying accelerometer and gyroscope measurements from a sample patient's nondominant hand during walking, collected before and after successful treatment.
  • Figure 3: Mobile app user interface. Patient information screen (left) allows entry of subject ID, treatment phase, phone placement, and activity selection. "Walk" activity recording screen (right) displays instructions and controls for data collection.
  • Figure 4: Design and architecture of the transformer-based activity classification model used in the DETECT Framework.

Theorems & Definitions (1)

  • Definition 1: Treatment Effect Score