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A Clinical Decision Support System with an Integrated EMR for Diagnosis of Peripheral Neuropathy

  • Patient Facing Systems
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Abstract

The prevalence of peripheral neuropathy in general population is ever increasing. The diagnosis and classification of peripheral neuropathies is often difficult as it involves careful clinical and electro-diagnostic examination by an expert neurologist. In developing countries a large percentage of the disease remains undiagnosed due to lack of adequate number of experts. In this study a novel clinical decision support system has been developed using a fuzzy expert system. The study was done to provide a solution to the demand of systems that can improve health care by accurate diagnosis in limited time, in the absence of specialists. It employs a graphical user interface and a fuzzy logic controller with rule viewer for identification of the type of peripheral neuropathy. An integrated medical records database is also developed for the storage and retrieval of the data. The system consists of 24 input fields, which includes the clinical values of the diagnostic test and the clinical symptoms. The output field is the disease diagnosis, whether it is Motor (Demyelinating/Axonopathy) neuropathy, sensory (Demyelinating/Axonopathy) neuropathy, mixed type or a normal case. The results obtained were compared with the expert’s opinion and the system showed 93.27 % accuracy. The study aims at showing that Fuzzy Expert Systems may prove useful in providing diagnostic and predictive medical opinions. It enables the clinicians to arrive at a better diagnosis as it keeps the expert knowledge in an intelligent system to be used efficiently and effectively.

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References

  1. Woolf, C. J., and Mannion, R. J., Neuropathic pain: Aetiology, symptoms, mechanisms, and management. Lancet 353(9168):1959–1964, 1999.

    Article  Google Scholar 

  2. Boulton, A. J. M., Gries, F. A., and Jervell, J. A., Guidelines for the diagnosis and outpatient management of diabetic peripheral neuropathy. Diab. Med. 15(6):508–514, 1998.

    Article  Google Scholar 

  3. Martyn, C. N., and Hughes, R. A., Epidemiology of peripheral neuropathy. J. Neurol. Neurosurg. Psychiatry 62(4):310–318, 1997.

    Article  Google Scholar 

  4. Spencer, P.S., Miller, M.S., Ross, S.M., Schwab, B.W., and Sabri, M.I., Biochemical mechanisms underlying primary degeneration of axons. Alterations of Metabolites in the Nervous System. Springer. 31–65, 1985.

  5. Jensen, P. G., and Larson, J. R., Management of painful diabetic neuropathy. Drugs Aging 18(10):737–749, 2001.

    Article  Google Scholar 

  6. Van Brakel, W. H., Peripheral neuropathy in leprosy and its consequences. Lepr. Rev. 71(Suppl):S146–S153, 2000.

    Google Scholar 

  7. Behse, F., Buchthal, F., and Carlsen, F., Nerve biopsy and conduction studies in diabetic neuropathy. J. Neurol. Neurosurg. Psychiatry 40(11):1072–1082, 1977.

    Article  Google Scholar 

  8. Chemali, K. R., and Tsao, B., Electrodiagnostic testing of nerves and muscles: When, why, and how to order. Cleve. Clin. J. Med. 72(1):37–48, 2005.

    Article  Google Scholar 

  9. Perkins, B. A., Olaleye, D., Zinman, B., and Bril, V., Simple screening tests for peripheral neuropathy in the diabetes clinic. Diabetes Care 24(2):250–256, 2001.

    Article  Google Scholar 

  10. Cheah, B. C., and Kiernan, M. C., Neurophysiological methodologies: Diagnosis of peripheral nerve disease and assessment of pharmacological agents. Curr. Opin. Investig. Drugs 11(1):72–79, 2010.

    Google Scholar 

  11. Tesfaye, S., Boulton, A. J., Dyck, P. J., et al., Diabetic neuropathies: Update on definitions, diagnostic criteria, estimation of severity, and treatments. Diabetes Care 33(10):2285–2293, 2010.

    Article  Google Scholar 

  12. Musen, M.A., Shahar, Y., and Shortliffe, E.H., Clinical decision-support systems. Biomedical informatics. Springer New York: 698–736, 2006.

  13. Faust, O., Acharya, U. R., and Tamura, T., Formal design methods for reliable computer-aided diagnosis: A review. IEEE Rev. Biomed. Eng. 5:15–28, 2012.

    Article  Google Scholar 

  14. Pradhan, N., Sadasivan, P. K., and Arunodaya, G. R., Detection of seizure activity in EEG by an artificial neural network: A preliminary study. Comput. Biomed. Res. 29(4):303–313, 1996.

    Article  Google Scholar 

  15. Forsstrom, J. J., and Dalton, K. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27(5):509–517, 1995.

    Article  Google Scholar 

  16. Phuong, N. H., and Kreinovich, V., Fuzzy logic and its applications in medicine. Int. J. Med. Informat. 62(2):165–173, 2001.

    Article  Google Scholar 

  17. Miller, R. A., Medical diagnostic decision support systems-past, present, and future a threaded bibliography and brief commentary. J. Am. Med. Inform. Assoc. 1(1):8–27, 1994.

    Article  Google Scholar 

  18. Garg, A. X., Adhikari, N. K., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J., Sam, J., and Haynes, R. B., Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. J. Am. Med. Assoc. 293(10):1223–1238, 2005.

    Article  Google Scholar 

  19. Zadeh, L. A., The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets Syst. 11(1):197–198, 1983.

    Article  MathSciNet  Google Scholar 

  20. Ramnarayan, P., and Britto, J., Paediatric clinical decision support systems. Arch. Dis. Child. 87(5):361–362, 2002.

    Article  Google Scholar 

  21. Zolnoori, M., Zarandi, M. H., Moin, M., and Teimorian, S., Fuzzy rule-based expert system for assessment severity of asthma. J. Med. Syst. 36(3):1707–1717, 2012.

    Article  Google Scholar 

  22. Kimura, J., Principles and pitfalls of nerve conduction studies. Ann. Neurol. 16(4):415–429, 1984.

    Article  Google Scholar 

  23. Buckley, J. J., Siler, W., and Tucker, D., A fuzzy expert system. Fuzzy Set Syst. 20(1):1–16, 1986.

    Article  Google Scholar 

  24. Keshwani, D. R., Jones, D. D., Meyer, G. E., and Brand, R. M., Rule-based Mamdani-type fuzzy modeling of skin permeability. Appl. Soft Comput. 8(1):285–294, 2008.

    Article  Google Scholar 

  25. Clancey, W. J., The epistemology of a rule-based expert system-a framework for explanation. Artif. Intell. 20(3):215–251, 1983.

    Article  MathSciNet  Google Scholar 

  26. Wang, L. X., and Mendel, J. M., Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. Syst. Hum. 22(6):1414–1427, 1992.

    Article  MathSciNet  Google Scholar 

  27. Ting, Y., Lu, W. B., Chen, C. H., and Wang, G. K., A fuzzy reasoning design for fault detection and diagnosis of a computer-controlled system. Eng. Appl. Artif. Intell. 21(2):157–170, 2008.

    Article  Google Scholar 

  28. Saade, J. J., and Diab, H. B., Defuzzification techniques for fuzzy controllers. IEEE Trans. Syst Man. Cybern.B Cybern 30(1):223–229, 2000.

    Article  Google Scholar 

  29. Brenner, H. E. R. M., and Gefeller, O. L. A. F., Variation of sensitivity, specificity, likelihood ratios and predictive values with disease prevalence. Stat. Med. 16(9):981–991, 1997.

    Article  Google Scholar 

  30. Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., Samsa, G., Hasselblad, V., Williams, J. W., Musty, M. D., Wing, L., Kendrick, A. S., Sanders, G. D., and Lobach, D., Effect of clinical decision-support systems: A systematic review. Ann. Intern. Med. 157(1):29–43, 2012.

    Article  Google Scholar 

  31. Kawamoto, K., Houlihan, C. A., Balas, E. A., and Lobach, D. F., Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. Br. Med. J. 330(7494):765, 2005.

    Article  Google Scholar 

  32. Ganesan, K., Acharya, R. U., Chua, C. K., Min, L. C., Mathew, B., and Thomas, A. K., Decision support system for breast cancer detection using mammograms. Proc. Inst. Mech. Eng. H. 227(7):721–732, 2013.

    Article  Google Scholar 

  33. Lahsasna, A., Ainon, R. N., Zainuddin, R., and Bulgiba, A., Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J. Med. Syst. 36(5):3293–3306, 2012.

    Article  Google Scholar 

  34. Das, S., Chowdhury, S. R., and Saha, H., Accuracy enhancement in a fuzzy expert decision making system through appropriate determination of membership functions and its application in a medical diagnostic decision making system. J. Med. Syst. 36(3):1607–1620, 2012.

    Article  Google Scholar 

  35. Singh, S., Kumar, A., Panneerselvam, K., and Vennila, J. J., Diagnosis of arthritis through fuzzy inference system. J. Med. Syst. 36(3):1459–1468, 2012.

    Article  Google Scholar 

  36. Sharaf-El-Deen, D. A., Moawad, I. F., and Khalifa, M. E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38(2):1–11, 2014.

    Article  Google Scholar 

  37. Acharya, U. R., Molinari, F., Sree, S. V., Chattopadhyay, S., Ng, K. H., and Suri, J. S., Automated diagnosis of epileptic EEG using entropies. Biomed. Signal Proc. Contr. 7(4):401–408, 2012.

    Article  Google Scholar 

  38. Kannathal, N., Acharya, U. R., Ng, E. Y. K., Krishnan, S. M., Min, L. C., and Laxminarayan, S., Cardiac health diagnosis using data fusion of cardiovascular and haemodynamic signals. Comput. Methods Appl. Mech. Eng. 82(2):87–96, 2006.

    Google Scholar 

  39. Durieux, P., Nizard, R., Ravaud, P., Mounier, N., and Lepage, E., A clinical decision support system for prevention of venous thromboembolism. J. Am. Med. Assoc. 283(21):2816–2821, 2000.

    Article  Google Scholar 

  40. Kannathal, N., Lim, C. M., Rajendra Acharya, U., and Sadasivan, P. K., Cardiac state diagnosis using adaptive neuro-fuzzy technique. Med. Eng. Phys. 28(8):809–815, 2006.

    Article  Google Scholar 

  41. Hunt, D. L., Haynes, R. B., Hanna, S. E., and Smith, K., Effects of computer-based clinical decision support systems on physician performance and patient outcomes: A systematic review. J. Am. Med. Assoc. 280(15):1339–1346, 1998.

    Article  Google Scholar 

  42. Godil, S. S., Shamim, M. S., Enam, S. A., and Qidwai, U., Fuzzy logic: A “simple” solution for complexities in neurosciences? Surg. Neurol. Int. 2:24, 2011.

    Article  Google Scholar 

  43. Kunhimangalam, R., Ovallath, S., and Joseph, P. K., A novel fuzzy expert system for the identification of severity of carpal tunnel syndrome. Biomed. Res. Int. 2013:846780, 2013.

    Article  Google Scholar 

  44. Kunhimangalam, R., Ovallath, S., and Joseph, P. K., Computer aided diagnostic problem solving: Identification of peripheral nerve disorders. IRBM. 34(3):244–251, 2013.

    Article  Google Scholar 

  45. Kunhimangalam, R., Ovallath, S., and Joseph, P.K., Artificial Neural Networks in the Identification of Peripheral Nerve Disorders. J. Mech. Med. Biol. 12(04):1240018(1–14), 2012.

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The authors declare that they have no conflicts of interest.

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Authors and Affiliations

  1. National Institute of Technology, NIT Calicut (PO), Kozhikode, Kerala, India, 673601

    Reeda Kunhimangalam & Paul K. Joseph

  2. Department of Neurology, Kannur Medical College, Anjarakandy (PO), Kerala, India

    Sujith Ovallath

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  1. Reeda Kunhimangalam
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  2. Sujith Ovallath
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Correspondence to Reeda Kunhimangalam.

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Kunhimangalam, R., Ovallath, S. & Joseph, P.K. A Clinical Decision Support System with an Integrated EMR for Diagnosis of Peripheral Neuropathy. J Med Syst 38, 38 (2014). https://doi.org/10.1007/s10916-014-0038-9

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