Designing a Decision Support System to Diagnose Neonatal Clinical PICC Infection Using Fuzzy Logic Designing a Decision Support System to Diagnose Neonatal Clinical PICC Infection Using Fuzzy Logic

Abstract

Introduction: Peripherally inserted central catheters (PICC) have entered neonatal intensive care units (NICU) as an instrument to reach blood vessels [1]. Compared with central and peripheral venous catheters, PICCs have considerably reduced the side effects and complications [2-4]. The same instruments can be the cause of catheter-related bloodstream infection (CRBSI)[5]. The purpose of this study is to create a fuzzy expert system for the early diagnosis of catheter infection in newborns. Materials and Methods: Factors effective in infection diagnosis were determined by a questionnaire and based on pediatric subspecialists?óÔé¼Ôäó 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 was assessed by the data of newborns?óÔé¼Ôäó files in one of the hospitals in Tehran. Results: Following the assessment, the sensitivity of the system turned out to be 95% and its Specificity and accuracy were 88 and 91 percent, respectively. Conclusion: Non-specificity of clinical signs and laboratory findings of newborns?óÔé¼Ôäó blood infection made its diagnosis difficult and uncertain. Using the designed expert system can be effective in the diagnosis of catheter-related blood infection.