Consistent Metric Learning for Outcomes of Different Measurement Tools of Cervical Spondylosis: Towards Better Therapeutic Effectiveness Evaluation

  • Gang Zhang
  • Ying Huang
  • Yingchun Zhong
  • Wuwei Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

Various measuring tools have been developed to evaluate the therapeutic effectiveness of neck pain caused by cervical spondylosis (CS). However, due to different evaluation bias of these tools, it has been observed empirically that there may be some inconsistency between the outcomes of different measuring tools, leading to great challenges to precisely evaluate therapeutic effectiveness. We propose to apply a supervised metric learning algorithm to learn a metric integrating the concerned outcomes of measuring tools with least inconsistency according to the training set. Through a pair-wise constraints metric learning algorithm, the metric is expressed as a parameterized transformation. We evaluate the learnt metric and the original outcomes with three well-known learning models on the clinical data from a multi-center clinical study on acupuncture for neck pain caused by CS. The result shows that the learned integrated metric has better performance than the original outcomes.

Keywords

Cervical spondylosis Metric learning Pair-wise constraints Therapeutic effectiveness evaluation 

Notes

Acknowledgments

This work is supported by the 2012 College Student Career and Innovation Training Plan Project (1184512043), the 2011 Higher Education Research Fund of GDUT (2011YZ09) and Science and Technology Planning Project of Haizhu District, Guangzhou (2011-YL-05).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Gang Zhang
    • 1
  • Ying Huang
    • 1
  • Yingchun Zhong
    • 1
  • Wuwei Wang
    • 1
  1. 1.School of AutomationGuangdong University of TechnologyGuangzhouChina

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