Abstract
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.
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Zhang, J., Gross, D., Zaïane, O.R. (2013). On the Application of Multi-class Classification in Physical Therapy Recommendation. In: Li, J., et al. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40319-4_13
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DOI: https://doi.org/10.1007/978-3-642-40319-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40318-7
Online ISBN: 978-3-642-40319-4
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