A Proposed Artificial Intelligence Algorithm for Assessing of Risk Priority for Medical Equipment in Iraqi Hospital
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How to Cite

A Proposed Artificial Intelligence Algorithm for Assessing of Risk Priority for Medical Equipment in Iraqi Hospital. (2009). Al-Khwarizmi Engineering Journal, 5(1), 71-82. https://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/521

Abstract

This paper presents a robust algorithm for the assessment of risk priority for medical equipment based on the calculation of static and dynamic risk factors and Kohnen Self Organization Maps (SOM). Four risk parameters have been calculated for 345 medical devices in two general hospitals in Baghdad. Static risk factor components (equipment function and physical risk) and dynamics risk components (maintenance requirements and risk points) have been calculated. These risk components are used as an input to the unsupervised Kohonen self organization maps. The accuracy of the network was found to be equal to 98% for the proposed system. We conclude that the proposed model gives fast and accurate assessment for risk priority and it works as promising tool for risk factor assessment for the service departments in large hospitals in Iraq.

 

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References

[1] M. L. Gullikson , “Risk Factors, Safety, and Management of Medical Equipment”, chapter in The Biomedical Engineering Handbook Third Edition Medical Devices and Systems, CRC Press is an imprint of Taylor & Francis Group, pp. 76-1to 76-16, 2006.
[2] M. L. Gullikson, “Biotechnology Procurement and Maintenance II: Technology Risk Management”, Third Annual International Pediatric Colloquium, Houston, Texas, 1994
[3] Y. David, “ Medical Technology 2001”, Health Care Conference, Texas Society of Certified Public Accountants, San Antonio, 1992.
[4] A. H. Ali Al-Timemy, F. M Al-Naima and S. S. Mehdi, “Data acquisition system for myocardial infarction classification based on wavelets and neural networks,” in proceedings of the Fifth International Multi-Conference on Systems, Signals and Devices (IEEE SSD'08), Jordan, 2008, pp. 2-4.
[5] M. Chester , “ Neural networks: a tutorial”, Englewood Cliffs, NJ: Prentice Hall, 1993.
[6] K. Hornik, M. Stinchcombe and H. White, “ Multilayer feed forward networks are universal approximators Neural Networks,” Journal of Neural Networks, Vol. 2, No. 5., pp. 359-366, 1989.
[7] J. A. Scott and E. L. Palmer, “Neural network analysis of ventilation-perfusion lung scans,” Journal of Radiology; Volume 186, pp. 661-664, 1993.
[8] W. G. Baxt “Use of an artificial neural network for the diagnosis of myocardial infarction”, Ann Intern Med, pp. volume 115, no. 11, pp. 843-848, Dec. 1991.
[9] J. V. Tu and M. R. J. Guerriere, “Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery”, presented at the 16th symposium on computer applications in medical care (SCAMC), Computers and Biomedical Research, volume 26, issue 3, pp. 220-229, 1993.
[10] M. Green, J. Bjork, J. Forberg, U. Ekelund, L. Edenbrandt and M. Ohlsson, “Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room,” Artificial Intelligence in Medicine, Volume 38, pp. 305–318, 2006.
[11] Peled, “Plasticity imbalance in mental disorders the neuroscience of psychiatry: Implications for diagnosis and research,” Medical Hypotheses, Volume 65, pp.947–952, 2005.
[12] E. Politi, C. Balduzzi, R. Bussi and L. Bellodi, “ Artificial neural networks: A study in clinical psychopharmacology,” Psychiatry Research, Volume 87, pp. 203–215, 1999.
[13] K. Suzuki, J. Shiraishi, H. Abe, H. MacMahon and K. Doi, “False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network,” Academic Radiology, Volume 12, pp. 191–201,2005.
[14] T. Batuello, E. J. Gamito, E. D. Crawford, M. Han, A. W. Partin, D. G. McLeod, et al., “Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer,” Urology, Volume 57, pp. 481–485, 2005.
[15] J. Dyro, “The Clinical Engineering Handbook”, Elsevier Academic Press, pp.235, 2001.
[16] M. L. Pecht and F. R. Nash, “ Predicting the reliability of electronic equipment”, Proceeding of IEEE, Vol. 82, issue no. 7, pp.990, 1994.
[17] T. Kohonen. “Self-Organization and Associative Memory”. Third Edition, Springer-Verlag, Berlin Heidelberg, 1989.
[18] T. Kohonen. “ Self-Organization Maps”. Springer-Verlag, Berlin Heidelberg, 1995.
[19] T. Yanaglda, T. Miura and I. Shioya, “Classifying news corpus by self-organizing maps”, presented at IEEE Pacific Rim Conference on Communications, Computers and signal Processing, PACRIM. 2003.
[20] A. H. A. Al Timemy, “A robust Algorithm for Ear Recognition System Based on Self Organization Maps”, 1st Regional Conference for Engineering Sciences, College of Engineering, Nahrain University, Baghdad, Nov., 2008.
[21] F. M. Ham and I. Kostanic, “Principles of Neurocomputing for Science and Engineering”, International Edition, McGraw-Hill Higher Education, 2001.

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