An artificial intelligence-based analysis will be performed using retrospective data of patients treated in adult intensive care units due to COVID-19. The dataset will include various parameters such as demographic information, laboratory results, vital signs, and clinical history. Among the machine learning models, logistic regression, support vector machines (SVM), decision trees, and deep learning techniques (e.g., artificial neural networks) will be utilized. The performance of these models will be compared with traditional scoring systems.

As a result of the analysis, it is anticipated that AI-based models will provide higher accuracy and reliability in mortality prediction. In particular, it is expected that deep learning-based models will better capture complex relationships and predict the outcomes of critically ill patients with greater precision. AI-supported data analysis results have the potential to guide diagnosis and treatment strategies in high-risk intensive care patients and can contribute to mortality prediction. AI-based approaches in intensive care are likely to offer significant advantages in the management of critical diseases such as COVID-19. These methods have the potential to improve clinical decision-making processes by providing healthcare professionals with more precise and timely information.

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.

Natural Language Processing (NLP) can analyze unstructured data, including patient notes and medical records, to provide additional insights into treatment efficacy and side effects. This capability expands the scope of retrospective studies and enhances the accuracy of the results. AI-based decision support systems can also offer recommendations to physicians on suitable treatments for specific patient profiles, integrating clinical guidelines and the latest scientific literature.

AI-based approaches offer significant advantages in the management of critical illnesses such as COVID-19, especially in intensive care units. These methods have the potential to improve clinical decision-making by providing healthcare professionals with more precise and timely information. Additionally, AI models can quickly adapt to emerging patterns by updating themselves with new data. However, ethical and technical challenges must be addressed carefully to ensure the widespread adoption of these approaches in clinical practice.

This study aims to generate promising and informative results through data analysis performed with artificial intelligence and machine learning on the records of COVID-19 patients in intensive care. The effectiveness of AI-based machine learning methods in predicting the mortality of critically ill patients treated in intensive care due to COVID-19 will be investigated. AI-based approaches provide higher accuracy and reliability compared to traditional methods and can contribute to improved outcomes in healthcare systems. These findings may highlight the potential of artificial intelligence applications in managing future pandemics such as COVID-19 and other critical illnesses.

Inclusion Criteria:

• All patients diagnosed with Covid-19 in the anesthesia and reanimation adult intensive care unit

Exclusion Criteria:

• Participants who do not meet the inclusion criteria stated above will be excluded from the study.

An artificial intelligence-based analysis will be performed using retrospective data of patients treated in adult intensive care due to COVID-19.The dataset will include various parameters such as demographic information, laboratory results, vital signs, and clinical history.Among the ML models, logistic regression, support vector machines (SVM), decision trees, and deep learning techniques (e.g., artificial neural networks) will be used. The performance of the models will be compared with traditional scoring systems.