An artificial intelligence-based analysis will be performed using retrospective data ofpatients treated in adult intensive care units due to COVID-19. The dataset will includevarious parameters such as demographic information, laboratory results, vital signs, andclinical history. Among the machine learning models, logistic regression, support vectormachines (SVM), decision trees, and deep learning techniques (e.g., artificial neuralnetworks) will be utilized. The performance of these models will be compared withtraditional scoring systems.As a result of the analysis, it is anticipated that AI-based models will provide higheraccuracy and reliability in mortality prediction. In particular, it is expected that deeplearning-based models will better capture complex relationships and predict the outcomesof critically ill patients with greater precision. AI-supported data analysis resultshave the potential to guide diagnosis and treatment strategies in high-risk intensivecare patients and can contribute to mortality prediction. AI-based approaches inintensive care are likely to offer significant advantages in the management of criticaldiseases such as COVID-19. These methods have the potential to improve clinicaldecision-making processes by providing healthcare professionals with more precise andtimely 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.
Kocaeli City Hospital
Izmit, Kocaeli, Turkey
Emine Yurt, Principal Investigator
MD