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Journal of Medical Systems

Erschienen in:

01.06.2012 | ORIGINAL PAPER

Prediction of Low Back Pain with Two Expert Systems

verfasst von: Murat Sari, Eyyup Gulbandilar, Ali Cimbiz

Erschienen in: Journal of Medical Systems | Ausgabe 3/2012

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Abstract

Low back pain (LBP) is one of the common problems encountered in medical applications. This paper proposes two expert systems (artificial neural network and adaptive neuro-fuzzy inference system) for the assessment of the LBP level objectively. The skin resistance and visual analog scale (VAS) values have been accepted as the input variables for the developed systems. The results showed that the expert systems behave very similar to real data and that use of the expert systems can be used to successfully diagnose the back pain intensity. The suggested systems were found to be advantageous approaches in addition to existing unbiased approaches. So far as the authors are aware, this is the first attempt of using the two expert systems achieving very good performance in a real application. In light of some of the limitations of this study, we also identify and discuss several areas that need continued investigation.

Langevin, H. M., and Sherman, K. J., Pathophysiological model for chronic low back pain integrating connective tissue and nervous system mechanisms. Med. Hypotheses 68(1):74–80, 2007.CrossRef

Gould, D., Kelly, D., Goldstone, L., and Gammon, J., Examining the validity of pressure ulcer risk assessment scales: developing and using illustrated patient simulations to collect data. J. Clin. Nurs. 10:697–706, 2001.CrossRef

Stener-Victorin, E., Lundeberg, T., Kowalski, J., Opdal, L., Sjöström, J., and Lundeberg, L., Perceptual matching for assessment of itch; reliability and responsiveness analyzed by a rank-invariant statistical method. J. Invest. Dermatol. 121:1301–1305, 2003.CrossRef

Richter, C. P., and Katz, D. T., Peripheral nerve injuries determined by the skin resistance method. JAMA 122:648, 1943.CrossRef

Riley, L. H., and Richter, C. P., Use of electrical skin resistance method in the study of patients with neck and upper extremity pain. Johns Hopkins Med. J. 137:69–74, 1975.

Liszka-Hackzell, J. J., and Martin, D. P., Categorization and analysis of pain and activity in patients with low back pain using a neural network technique. J. Med. Syst. 26(4):337–347, 2002.CrossRef

Shankar, K., Bharathi, V. S., and Daniel, J., An empirical approach for objective pain measurement using dermal and cardiac parameters. ICBME 2008(23):678–681, 2009.

Weng, C. S., Tsai, Y. S., Shu, S. H., Chen, C. C., and Sun, M. F., The treatment of upper back pain by two modulated frequency modes of acupuncture-like tens. J. Med. Biol. Eng. 25(1):21–25, 2005.

Weng, C. S., Tsai, Y. S., and Yang, C. Y., Using electrical conductance as the evaluation parameter of pain in patients with low back pain treated by acupuncture like tens. Biomed. Eng. Appl. Basis Commun. 16:205–212, 2004.CrossRef

10.

Bounds, D. G., Lloyd, P. J., Mathew, B., and Waddell, G., A multi layer perceptron network for the diagnosis of low back pain. IEEE Int. Conf. Neural Netw. 2:481–489, 1988.CrossRef

11.

Lin, L., Hu, P. J. H., and Sheng, O. R. L., A decision support system for lower back pain diagnosis: uncertainty management and clinical evaluations. Decis. Support Syst. 42:1152–1169, 2006.CrossRef

12.

Gioftsos, G., and Grieve, D. W., The use of artificial neural networks to identify patients with chronic low-back pain conditions from patterns of sit-to-stand manoeuvres. Clin. Biomech. 11(5):275–280, 1996.CrossRef

13.

Vaughn, M. L., Cavill, S. J., Taylor, S. J., Foy, M. A., and Fogg, A. J. B. Direct explanations and knowledge extraction from a multilayer perceptron network that performs low back pain classification. In: Wermter, S., and Sun, R. (Eds.), Hybrid Neural Systems. Lecture Notes in Artificial Intelligence. Springer, 2000.

14.

Mathew, B., Morris, D., David, H., and Gordon, W., Artificial intelligence in the diagnosis of low-back pain and sciatica. Spine 13(2):168–172, 1988.CrossRef

15.

Li, B., Yan, C., and Xu, Y. Designing and implementing of an expert system for the differential diagnosis and treatment of lumbago. Proceedings of the 3rd International Conference on Young Computer Scientists. Beijing, 1027–1028, 1993.

16.

Forsstrom, J. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27:509–517, 1995.CrossRef

17.

Carregal, A., Figueira, A., Núñez, M., Carollo, A., Lorenzo, A., Rey, M., and González, G., Fuzzy logic and postoperative pain. Rev. Esp. Anestesiol. Reanim. 44(6):215–217, 1997.

18.

Shieh, J. S., Dai, C. Y., Wen, Y. R., and Sun, W. Z., A novel fuzzy pain demand index derived from patient-controlled analgesia for postoperative pain. IEEE Trans. Biomed. Eng. 54(12):2123–2132, 2007.CrossRef

19.

Shamim, M. S., Enam, S. A., and Qidwai, U., Fuzzy Logic in neurosurgery: predicting poor outcomes after lumbar disk surgery in 501 consecutive patients. Surg. Neurol. 72(6):565–572, 2009.CrossRef

Titel: Prediction of Low Back Pain with Two Expert Systems
verfasst von: Murat Sari
Eyyup Gulbandilar
Ali Cimbiz
Publikationsdatum: 01.06.2012
Verlag: Springer US
Erschienen in: Journal of Medical Systems / Ausgabe 3/2012
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-010-9613-x

Springer Medizin

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