Last reviewed · How we verify
NCT06795880
"Artificial Intelligence-Based Data Analysis Results and Mortality Prediction in Covid-19 Patients in Intensive Care"
trial in COVID-19 in 400 participants. Participants enrolled and being followed up; not accepting new ones.
1 April 2024
Quick facts
| Lead sponsor | Kocaeli City Hospital |
|---|---|
| Status | Active, enrolled |
| Study type | OBSERVATIONAL |
| Enrollment | 400 |
| Start date | 1 April 2020 |
| Primary completion | 1 April 2024 |
| Estimated completion | 1 April 2025 |
| Sites | 1 location across Turkey (Türkiye) |
Conditions studied
- COVID-19 — all drugs for COVID-19 →
Sponsor
Kocaeli City Hospital
Who can join
18 and older, any sex, with COVID-19. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT06795880
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Trials by the same sponsor.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT06795880 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Kocaeli City Hospital
- Last refreshed: 28 January 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT06795880.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing