"Artificial Intelligence-Based Data Analysis Results and Mortality Prediction in Covid-19 Patients in Intensive Care"

In this research, patient information obtained through retrospective archive scanning in high-risk patients followed up in the Anesthesia Intensive Care Unit with a diagnosis of COVID-19 will be recorded, and data analysis will be performed using artificial intelligence-based machine learning methods. The applicability of the obtained results in predicting the morbidity and mortality of patients, as well as the accuracy and reliability of these data, will be discussed.

The COVID-19 pandemic has strained healthcare systems worldwide, creating significant challenges in the management of critically ill patients, particularly in adult anesthesia intensive care units. During this process, accurately predicting the morbidity and mortality risks of patients is essential for the effective use of healthcare resources and the improvement of patient care. Traditional mortality prediction methods are generally based on clinical scoring systems and manual analysis of patient data. However, the accuracy and reliability of these methods remain limited. Artificial intelligence (AI) and machine learning (ML) techniques offer promising results in this field due to their capacity to analyze large datasets and detect complex patterns.

AI applications are used in various ways to evaluate and improve the effectiveness of intensive care treatments in COVID-19 patients. In this context, AI can be utilized in critically ill intensive care patients. Based on current information, AI can be applied to tasks such as data collection, data analysis and modeling, prognostic model development, result visualization, natural language processing (NLP), and decision support systems.

In data collection, AI algorithms can be employed to gather large and diverse datasets from hospitals quickly and accurately. This minimizes data inconsistencies and omissions, enhancing the accuracy of the study. Machine learning algorithms can analyze various variables such as patients' demographic data, disease severity, treatment protocols, and outcomes to determine the factors with the greatest impact on mortality. Algorithms such as regression models, decision trees, and random forests are commonly used for this purpose.

AI-based prognostic models can be developed using patient data to predict the most effective treatment for individual patients. These models can support optimization of treatment decisions by predicting patient responses to treatment. Furthermore, AI-powered visualization tools, such as interactive graphs and heat maps, can assist in understanding and interpreting study findings by highlighting relationships between treatment responses and mortality rates.