AIMTo design a fuzzy expert system to help detect and diagnose the severity of diabetic neuropathy.METHODSThe research was completed in 2014 and consisted of two main phases. In the first phase, the diagnostic parameters were determined based on the literature review and by investigating specialists’ perspectives (n = 8). In the second phase, 244 medical records related to the patients who were visited in an endocrinology and metabolism research centre during the first six months of 2014 and were primarily diagnosed with diabetic neuropathy, were used to test the sensitivity, specificity, and accuracy of the fuzzy expert system.RESULTSThe final diagnostic parameters included the duration of diabetes, the score of a symptom examination based on the Michigan questionnaire, the score of a sign examination based on the Michigan questionnaire, the glycolysis haemoglobin level, fasting blood sugar, blood creatinine, and albuminuria. The output variable was the severity of diabetic neuropathy which was shown as a number between zero and 10, had been divided into four categories: absence of the disease, (the degree of severity) mild, moderate, and severe. The interface of the system was designed by ASP.Net (Active Server Pages Network Enabled Technology) and the system function was tested in terms of sensitivity (true positive rate) (89%), specificity (true negative rate) (98%), and accuracy (a proportion of true results, both positive and negative) (93%).CONCLUSIONThe system designed in this study can help specialists and general practitioners to diagnose the disease more quickly to improve the quality of care for patients.
There is ample evidence that the pharmacokinetics of drugs in infants and children may differ markedly from those in adults. The goals of phannacokinetics is to maximize efficacy, minimize toxicity and drug interactions, enhance compliance, and reduce cost of medications. Studies with drugs including anticonvulsants, antimicrobials, digoxin, methotrexate and theophylline have demonstrated that these goals are achievable. Genetic and racial background, underlying disease, concurrent drugs, and nutritional status can influence pharmacokinetics. Population pharmacokinetics can provide useful data even when limited number of samples are available from a large number of patients. Pharmacokinetic monitoring is influenced by numerous factors including dose and dosage form, method of drug administration, times of sample collection, analytical and forecasting methods, and implementation of dosage recommendations. The future of pharmacokinetics, in large part would depend on correlating its parameters to the markers of efficacy and/or toxicity of medications. The ultimate goal of pharmacokinetics should be to improve the quality of life and health outcomes in infants and children.
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