Not Logged In

On the application of multi-class classification in physical therapy recommendation

Full Text: danth2013.pdf PDF

Recommending optimal rehabilitation intervention for injured workers that would lead to successful return-to-work (RTW) is a challenge for clinicians. Currently, the clinicians are unable to identify with complete confidence which intervention is best for a patient and the referral is often made in trial and error fashion. Only 58% recommendations are successful in our dataset. We aim to develop an interpretable decision support system using machine learning to assist the clinicians. We use various re-sampling techniques to tackle the multi-class imbalance and class overlap problem in real world application data. The final model has shown promising potential in classification compared to human baseline and has been integrated into a web-based decision-support tool that requires additional validation in a clinical sample.

Citation

J. Zhang, D. Gross, O. Zaiane. "On the application of multi-class classification in physical therapy recommendation". Workshop on Data Analytics for Targeted Healthcare, April 2013.

Keywords: multi-class imbalance, re-sampling, clinical decision-support, rule-based learning
Category: In Workshop

BibTeX

@misc{Zhang+al:13,
  author = {Jing Zhang and Douglas P. Gross and Osmar R. Zaiane},
  title = {On the application of multi-class classification in physical therapy
    recommendation},
  booktitle = {Workshop on Data Analytics for Targeted Healthcare},
  year = 2013,
}

Last Updated: January 13, 2020
Submitted by Sabina P

University of Alberta Logo AICML Logo