Abstract
Urinary tract infections (UTIs) are among the most common bacterial infections and account for a significant part of the workload in clinical microbiology laboratories. Hence, urine is the specimen most frequently submitted for culture. Physicians distinguish UTIs from other diseases that have similar clinical presentations with use of a small number of tests to distinguish bacteriuria. The microbiological examination of urine consists of examining a methylene smear of the urine specimen, a screening test of significant bacteriuria and culture. In the smear one or more bacterial cells per oil-immersion field usually implies that there are 105 or more bacteria per milliliter in the specimen, the number of RBC and WBC is also a very important indicator. In literature, the normal ranges of these variables are differently defined. The analysis of this data could be very simplified using data management systems. Fuzzy logic, in a narrow sense, is a logical system, which is an extension of multivalued logic. The fuzzy logic works on a theory which relates to classes of objects with blurred boundaries in which membership is a matter of degree. This enables fuzzy systems applicable to broad range of parameters and expected output values in many aspects of science. The aim of this study was to create a fuzzy model, in the MATLAB environment, to aid physicians in interpreting the results of the microscopic urine analysis, considering the number of bacteria, RBC and WBC as well as turbidity of the sample.
The original version of this chapter was revised: The spacing error in fifth author’s name has been corrected.
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References
Wilson M L, Gaido L (2004). Laboratory diagnosis of urinary tract infections in adult patients. Clinical Infectious Diseases, 38(8): 1150—1158.
Ronald A (2003). The etiology of urinary tract infection: traditional and emerging pathogens. Disease-a-Month, 49(2), 71-82.
Arslan H, Azap Ö K, Ergönül Ö et al (2005) Risk factors for ciprofloxacin resistance among Escherichia coli strains isolated from community-acquired urinary tract infections in Turkey. Journal of Antimicrobial Chemotherapy, 56(5), 914-918.
Wagenlehner, Florian M E, Naber Kurt G (2006). Treatment of bacterial urinary tract infections: presence and future. European urology.,49(2): 235—244.
Vandepitte J, Verhaegen J, Engbaek K et al (2003). Basic laboratory procedures in clinical bacteriology Ed 2. World Health Organization.
Pollock C, Liu P L, Gyory A Z, et al (1989). Dysmorphism of urinary red blood cells–value in diagnosis. Kidney international. 36(6): 1045—1049.
Leibovici L, Fishman M, Schonheyder H C et al (2000). A causal probabilistic network for optimal treatment of bacterial infections. IEEE Transactions on Knowledge and Data Engineering, 12(4), 517-528.
Kao H Y, Li H L (2005). A diagnostic reasoning and optimal treatment model for bacterial infections with fuzzy information. Computer methods and programs in biomedicine, 77(1), 23-37.
Alayón S, Robertson R, Warfield S K et al (2008). A Fuzzy System for Helping Medical Diagnosis of Malform-ations of Cortical Development. J Biomed Inform. PMC.
Beth A, Sproule B A, Naranjo C A et al (2002). Fuzzy pharmacology: Theory and application. Trends Pharmacology. Sci., 23 (9): 412-417.
Papageorgiou E I (2012). Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Computer methods and programs in biomedicine, 105(3), 233-245.
MATLAB, The Math Works, Inc., Natick, Massachusetts, United States.
https://www.mathworks.com/help/fuzzy/what-is-fuzzy-logic.html
Baig F, Khan M S, Noor Y et al (2011). International Journal on Computer Science and Engineering(IJCSE),3(5): 2093.
Poongodi M, Manjula L, Pradeepkumar S et al (2012). Cancer Prediction Techniques using Fuzzy Logic. International Journal of Current Research. 4 (02): . 106-110
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Ibrisimovic, M.A., Karlı, G., Balkaya, H.E., Ibrisimovic, M., Hukic, M. (2017). A Fuzzy Model to Predict Risk of Urinary Tract Infection. In: Badnjevic, A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-10-4166-2_43
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DOI: https://doi.org/10.1007/978-981-10-4166-2_43
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