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

NCT06795880 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2025-01-28

No results posted yet for this study

Summary

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.

Conditions

Sponsors & Collaborators

  • Kocaeli City Hospital

    lead OTHER_GOV

Principal Investigators

  • Emine Yurt · MD

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-04-01
Primary Completion
2024-04-01
Completion
2025-04-01

Countries

  • Turkey (Türkiye)

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06795880 on ClinicalTrials.gov