Hybrid Expert System Advisor for Anaestetic Control and Intense Care Using Adaptive Neuro Fuzzy Inference System and Certainty Factors

  • Authors

    • Baidaa M Alsafy
    • M Alsafy Lateef Jaheel
    • Amir Y Mahdi
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.25.27007
  • Expert system, anaesthesia, fuzzy control, ANFIS, certainty factor.
  • Abstract

    Despite the great advances in medicine and technology, there are some risks to the life of the patient suffering from anesthesia and intensive care, and the reason that the human has a restricted capability to continuously and accurately analyse huge amount of patients data. Most methods previously used do not give accurate results because they use a single pointer. Therefore, in this research, many artificial intelligence techniques and quantitative measurements have been merge into an a to support doctors decision in controlling anesthesia and intensive care. This research was designed as an intelligent hybrid system as an anesthesia consultant by incorporating of rule-based, adaptive neuro-fuzzy inference system, fuzzy control and certainty factor theory that can simulate an anesthesiologist in thinking and making appropriate decisions in complex circumstances. The aim of this research is to improve clinical diagnosis and detect critical events during anesthesia by relying on artificial intelligence methods.

     

     

  • References

    1. [1] Hemmerling, T. M. (2011). Decision Support Systems in Anesthesia, Emergency Medicine and Intensive Care Medicine, in Practice and Challenges in Biomedical Related Domain, J.S. C., Editor, InTech, 239-260.

      [2] Cooper JB, Newbower RS, Long CD, McPeek B. Preventable anesthesia mishaps: a study of human factors. Qual Safety Health Care 2002; 11(3): 277–282.

      [3] Miller GA. The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 1956; 101(2): 343–352.

      [4] Gunther P, Kimiko F, Volker H, Peter M, Andreas V, Luzius B, Andrea K, Yoshihisa F, Daniel I, Daniel L. Automatic algorithm for monitoring systolic pressure variation and difference in pulse pressure. Int Anesth Res Soc Anesth Analg 2009; 108(6): 1823–1829.

      [5] Ansermino JM, Jeremy PD, Randy T, Joanne L, Ping Y, Chris JB, Guy AD, John B. An evaluation of a novel software tool for detecting changes in physiological monitoring. Int Anesth Res Soc Anesth Analg 2009; 108(3): 873–880.

      [6] Bates, D. W., & Gawande, A. A. (2003). Improving Safety with Information Technology. New England Journal of Medicine, 348(25), 2526-2534.

      [7] Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1978; 1: 3–28.

      [8] Hanson CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001;29:427–435.[9]. Ying H, McEachern, Eddleman DW, Sheppard LC. Fuzzy control of mean arterial pressure in postsurgical patients withsodium nitroprusside infusion. IEEE Transactions on Biomedical Engineering 1992; 39: 1060–1070.

      [9] Kosko B, Isaka S. Fuzzy logic. Scientific American 1993; July: 62–67.

      [10] Phuong, Nguyen Hoang and Kreinovich, Vladik. “Fuzzy Logic and its Applications in Medicine†Asian Pacific Medical Informatics Conference APAMI-MIC,1 (2000)

      [11] Schuh, Ch. “Fuzzy Sets and their Application in Medicine†North American Fuzzy Information Processing Society, (2005)

      [12]

      [13] MICHAEL NEGNEVITSKY, Artificial Intelligence a Guid to Intelligent System, Second Edition 2005.

      [14] Polat K, Gunes S. An expert system approach based on principal component analysisand adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. J Digital Signal Process 2007; 17: 702-710.

      [15] D. P. S. Dewi, Sistem Pakar Diagnosa Penyakit Jantung dan Paru dengan Fuzzy Logic dan Certainty Factor, Merpati. Vol. 2, No. 3, 2014.

      [16] Wulandari, Pembuatan Sistem Pendukung Keputusan Berbasis Teori Fuzzy untuk Mengembangkan Suatu Produk Baru, Journal Sains Teknologi dan Industri, Vol 2., No. 2, 2005.

      [17] S. Kusumadewi, Artificial Intelligence (Teknik dan Aplikasinya), Yogyakarta: Graha Ilmu, 2003.

      [18] Julian M. Barker ,Simon L. Maguire ,Simon J. Mills and Abdul-Ghaaliq Lalkhen "The Clinical Anaesthesia Viva Book ",Second edition,2009

      [19] Umamaheswari, A., & Kumari, P. (2014). Fuzzy TOPSIS and Fuzzy VIKOR methods using the Triangular Fuzzy Hesitant Sets. International Journal of Computer Science Engineering and Information Technology Research, 4, 15-24.

      [20] Robert Detrano & M.D & PhD, V.A. Medical Center, Long Each and Cleveland Clinic Foundation. Available: www.archive.ics.uci.edu/ml/datasets/Heart+Disease

      [21] Rennels G.D and Miller P.L Artifical Intelligence Resarch In Anesthesia and Intensive Care. J Clin Monit 4, 274 (1988).

      [22] www.emedicinehealth.com

  • Downloads

  • How to Cite

    M Alsafy, B., Lateef Jaheel, M. A., & Y Mahdi, A. (2018). Hybrid Expert System Advisor for Anaestetic Control and Intense Care Using Adaptive Neuro Fuzzy Inference System and Certainty Factors. International Journal of Engineering & Technology, 7(4.25), 319-326. https://doi.org/10.14419/ijet.v7i4.25.27007

    Received date: 2019-02-02

    Accepted date: 2019-02-02

    Published date: 2018-11-30