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

NCT06795880 | Active Not Recruiting

• 1 other identifier: 2024-74
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 1

Brief 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

COVID-19

Keywords

intensive care; machine learning; mortality

Outcome Measures

Primary Outcomes (1)

• Mortality prediction in covid-19 patients in intensive care using artificial intelligence models

  Mortality prediction in covid-19 patients in intensive care using artificial intelligence models, analysis of patient data with artificial intelligence models.

  "through study completion, an average of 4 year"

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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.

You may qualify if:

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

Sponsors & Collaborators

• Kocaeli City Hospital: lead

Study Sites (1)

Kocaeli City Hospital

İzmit, Kocaeli, 41060, Turkey (Türkiye)

Location

Related Publications (1)

• Chee ML, Ong MEH, Siddiqui FJ, Zhang Z, Lim SL, Ho AFW, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. Int J Environ Res Public Health. 2021 Apr 29;18(9):4749. doi: 10.3390/ijerph18094749.

  PMID: 33947006 BACKGROUND

Related Links

• Improving prediction of COVID-19 mortality using machine learning in the Spanish SEMI-COVID-19 registry
• Identifying Predictors of COVID-19 Mortality Using Machine Learning

MeSH Terms

Conditions

COVID-19

Condition Hierarchy (Ancestors)

Pneumonia, Viral; Pneumonia; Respiratory Tract Infections; Infections; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Emine Yurt

  MD

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 24, 2025
• First Posted: January 28, 2025
• Study Start: April 1, 2020
• Primary Completion: April 1, 2024
• Study Completion: April 1, 2025
• Last Updated: January 28, 2025

Record last verified: 2025-01

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)