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Evidence-Based Complementary and Alternative Medicine
Volume 2013, Article ID 478202, 8 pages
Research Article

Comparisons of Prediction Models of Myofascial Pain Control after Dry Needling: A Prospective Study

1Nursing Department, Kaohsiung Armed Forces General Hospital, Kaohsiung 80201, Taiwan
2Section of Anesthesiology, Pingtung Christian Hospital, Pingtung 90059, Taiwan
3Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No. 100, Shih-Chuan 1st Road, Kaohsiung 80708, Taiwan

Received 3 December 2012; Accepted 10 June 2013

Academic Editor: José M. Climent Barberá

Copyright © 2013 Yuan-Ting Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. This study purposed to validate the use of artificial neural network (ANN) models for predicting myofascial pain control after dry needling and to compare the predictive capability of ANNs with that of support vector machine (SVM) and multiple linear regression (MLR). Methods. Totally 400 patients who have received dry needling treatments completed the Brief Pain Inventory (BPI) at baseline and at 1 year postoperatively. Results. Compared to the MLR and SVM models, the ANN model generally had smaller mean square error (MSE) and mean absolute percentage error (MAPE) values in the training dataset and testing dataset. Most ANN models had MAPE values ranging from 3.4% to 4.6% and most had high prediction accuracy. The global sensitivity analysis also showed that pretreatment BPI score was the best parameter for predicting pain after dry needling. Conclusion. Compared with the MLR and SVM models, the ANN model in this study was more accurate in predicting patient-reported BPI scores and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.