Reza Safdari; Maliheh Kadivar; Mahnaz Nazari; Mahbubeh Mohammadi
Volume 13, Issue 7 , December 2017, , Pages 446-452
Abstract
Introduction: Peripherally inserted central catheters (PICC) are utilized in neonatal intensive care units (NICUs) as an instrument to vascular access. The PICCs significantly reduce side effects compared to central and peripheral venous catheters and can be the cause of catheter-related bloodstream ...
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Introduction: Peripherally inserted central catheters (PICC) are utilized in neonatal intensive care units (NICUs) as an instrument to vascular access. The PICCs significantly reduce side effects compared to central and peripheral venous catheters and can be the cause of catheter-related bloodstream infections (CRBSIs). The purpose of this study was to create a fuzzy expert system for the early diagnosis of PICC-related infections in newborns.Methods: This descriptive-applied study was conducted in 2016. The statistical population of this research consisted of the medical files of newborns in Children's Medical Center in Tehran, Iran, and sampling was carried out using convenient sampling method. The research tools were a checklist and questionnaires. Factors affecting infection diagnosis were determined based on pediatric specialists’ comments. The system was designed bilingually (Persian and English) using C# software and SQL Server database. The output of the system is the percentage of infection risk. The system evaluations were carried out using data from the medical files of newborns in a hospital in Tehran, Iran. Data was analyzed using Excel software.Results: Based on system assessment and comparison of the system output with the diagnosis of the specialists, the sensitivity, specificity, and accuracy of the system were 95%, 88%, and 91%, respectively.Conclusion: The non-specificity of clinical signs and laboratory findings of blood infection in newborns have made its diagnosis difficult and uncertain. Using a designed expert system can be effective in the diagnosis of CRBSIs.
Reza Safdari; Leila Shahmoradi; Mojtaba Javaherzadeh; Mirmikail Mirhosseini
Volume 13, Issue 6 , November 2017, , Pages 399-404
Abstract
Introduction: Acute appendicitis is the most common cause of admittance of patients with abdominal pain to hospital and appendectomy is the most commonly performed emergency surgery. Despite significant advances in the field of diagnosis, a significant number of negative appendectomies are reported. ...
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Introduction: Acute appendicitis is the most common cause of admittance of patients with abdominal pain to hospital and appendectomy is the most commonly performed emergency surgery. Despite significant advances in the field of diagnosis, a significant number of negative appendectomies are reported. In this study, the design and evaluation of artificial neural networks to help diagnose acute appendicitis was investigated.Methods: In this descriptive study, variables affecting the diagnosis were identified through literature review. Then, these variables were categorized in the form of a checklist, and evaluated and prioritized by general surgery specialists. The sample size was determined as 181 cases to train and evaluate the performance of neural networks. The database was created using records of patients who had undergone appendectomy during 2015 in Modarres Hospital, Tehran, Iran. Then, different architectures of artificial multilayer perceptron (MLP) neural network were implemented and compared in MATLAB environment to determine the optimal diagnostic performance. Parameters such as specificity, sensitivity, and accuracy were used for network assessment.Results: Comparison of the optimal output of the MLP with pathological results showed that the sensitivity, specificity, and accuracy of the diagnosis network were 68.8%, 82%, and 78.5%, respectively. Based on the existing standards and the general surgeons’ opinions, the MLP network improved diagnostic accuracy for acute appendicitis.Conclusion: The designed MLP can model the performance of an expert with acceptable accuracy. The use of this MLP in clinical decision support systems can be useful in the reduction of negative references to medical centers, timely diagnosis, prevention of negative appendectomy, reduction of the duration of hospitalization, and reduction of medical expenses.
