Prediction of low back pain with two expert systems

Journal of Medical Systems

Volumn 36, Issue 3, 2012, Pages 1523-1527

  Sari, Murat ^a   Gulbandilar, Eyyup ^b   Cimbiz, Ali ^b  

^a PAMUKKALE UNIVERSITY   (Turkey)

^b DUMLUPINAR UNIVERSITY   (Turkey)

Author keywords

Adaptive neuro fuzzy inference system (ANFIS); Artificial neural network (ANN); Expert system; Low back pain; Modeling; Skin resistance; Visual analog scale

Indexed keywords

ADAPTIVE NEURO FUZZY INFERENCE SYSTEM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; EXPERT SYSTEM; HUMAN; LOW BACK PAIN; MAJOR CLINICAL STUDY; SKIN CONDUCTANCE; VISUAL ANALOG SCALE;

ADULT; AGED; EXPERT SYSTEMS; FEMALE; FUZZY LOGIC; HOSPITALS, UNIVERSITY; HUMANS; LOW BACK PAIN; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); PAIN MEASUREMENT; TURKEY;

EID: 84864045305     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-010-9613-x     Document Type: Article

References (19)

1. 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. (Pubitemid 44767614)
2. 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. (Pubitemid 33754797)
3. 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. (Pubitemid 38005476)
4. Richter, C. P., and Katz, D. T., Peripheral nerve injuries determined by the skin resistance method. JAMA 122:648, 1943.
5. 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.
6. 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. (Pubitemid 38438029)
7. 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.
8. 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.
9. 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.
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.
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. (Pubitemid 44537488)
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. (Pubitemid 26235696)
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.
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.
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. (Pubitemid 127513072)
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.
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.