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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yan Huang Huiru Zheng Nugent, C. McCullagh, P. Black, N. Vowles, K. McCracken, L. |
| Copyright Year | 2009 |
| Description | Author affiliation: University of Ulster, Jordanstown, BT37 0QB, UK (Yan Huang; Huiru Zheng; Nugent, C.; McCullagh, P.; Black, N.) || Centre for Pain Research, University of Bath, UK (McCracken, L.) || Haywood Hospital, Stoke-on-Trent, UK (Vowles, K.) |
| Abstract | Chronic pain is a common long-term condition that changes patients' physical and emotional functioning. Currently, the integrated biopsychosoical approach is the mainstay treatment for patients with chronic pain. Self reporting (the use of questionnaires) is one of the most common methods to evaluate treatment outcome. Nevertheless, a large number of questions (for example 329 questions in this study) may be required and as such may be viewed as not being convenient for patients to complete. This paper has applied the theory of information gain to rank the questions in addition to investigating important factors related to the treatment outcome. Analysis within the study ranked the questions from 1 to 329 based on information gain (highest to lowest). Results showed that questions related to chronic pain coping strategies and value-based actions had high information gain. Four supervised learning classifiers were used to investigate the correlations between feature numbers and classification accuracy. The results showed classifier that a Multi-layer Perceptron classifier obtained the highest classification accuracy (96.05%) on an optimized subset which consisted of 133 questions. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 1756659 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424453795 |
| DOI | 10.1109/ITAB.2009.5394419 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-04 |
| Publisher Place | Cyprus |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Pain Medical treatment Medical diagnostic imaging Medical services Biopsy Information analysis Supervised learning Multilayer perceptrons Modems Diabetes classification Feature selection information gain chronic pain self reporting |
| Content Type | Text |
| Resource Type | Article |
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