Artificial Intelligence With Determination of Central Venous Catheter Line Associated Infection Risk

The goal of this methodological, retrospective and prospective study is to; it is a tool to develop a risk estimator tool to detect risk gaps in individuals using artificial intelligence technology that is dangerous for those with CVC in adult intensive care patients, to test risk level estimation frameworks and to evaluate outcomes in the clinic. In our study, it is also our aim to protect, to present the security measures to prevent the risk of CVC with an artificial intelligence model, in an evidence-based way. The main question\[s\]it aims to answer are: * Can the risk of CVC-related infection be determined in adult intensive care patients using artificial intelligence? * To what degree of accuracy can the risk of CVC-associated infection be determined in adult intensive care patients using artificial intelligence? * What are the nursing practices that can reduce the risk of CVC-related infections? Methodology to develop an artificial intelligence-based CVC-associated infection risk level determination algorithm, retrospective using data from Electronic Health Records (EHR) patient data and manual patient files between January 2018 and December 2022 to create the algorithm and test the model accuracy, and the development stages of the model After the completion of the model, up-to-date data were collected for the use of the model and it was planned to be done prospectively.