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Proceedings Paper

A web-based neurological pain classifier tool utilizing Bayesian decision theory for pain classification in spinal cord injury patients
Author(s): Sneha K. Verma; Sophia Chun; Brent J. Liu
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Paper Abstract

Pain is a common complication after spinal cord injury with prevalence estimates ranging 77% to 81%, which highly affects a patient’s lifestyle and well-being. In the current clinical setting paper-based forms are used to classify pain correctly, however, the accuracy of diagnoses and optimal management of pain largely depend on the expert reviewer, which in many cases is not possible because of very few experts in this field. The need for a clinical decision support system that can be used by expert and non-expert clinicians has been cited in literature, but such a system has not been developed. We have designed and developed a stand-alone tool for correctly classifying pain type in spinal cord injury (SCI) patients, using Bayesian decision theory. Various machine learning simulation methods are used to verify the algorithm using a pilot study data set, which consists of 48 patients data set. The data set consists of the paper-based forms, collected at Long Beach VA clinic with pain classification done by expert in the field. Using the WEKA as the machine learning tool we have tested on the 48 patient dataset that the hypothesis that attributes collected on the forms and the pain location marked by patients have very significant impact on the pain type classification. This tool will be integrated with an imaging informatics system to support a clinical study that will test the effectiveness of using Proton Beam radiotherapy for treating spinal cord injury (SCI) related neuropathic pain as an alternative to invasive surgical lesioning.

Paper Details

Date Published: 19 March 2014
PDF: 8 pages
Proc. SPIE 9039, Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations, 90390E (19 March 2014); doi: 10.1117/12.2044434
Show Author Affiliations
Sneha K. Verma, The Univ. of Southern California (United States)
Sophia Chun, VA Long Beach Healthcare System (United States)
Brent J. Liu, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 9039:
Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations
Maria Y. Law; Tessa S. Cook, Editor(s)

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