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Neural Network-Based Prediction in Critical COVID-19 Patients

NCT07436572 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 113

Last updated 2026-02-27

No results posted yet for this study

Summary

In the context of an emerging pandemic without an established prognostic scoring system, deep learning approaches can be used to quickly develop empirical prognostic models.

This study aimed to present an artificial neural network (ANN) model to predict the duration of mechanical ventilation and mortality in COVID-19 patients at the intensive care unit.

Methods: Data were collected from medical records of 113 COVID-19 patients who had followed up at the intensive care unit between February 2020 and June 2020. An ANN approach was used to predict the length of mechanical ventilation and mortality in COVID-19 patients by evaluating patients' clinical data (demographic, laboratory, and comorbidities).

Conditions

  • COVID-19 Pandemic

Interventions

OTHER

Artificial Neural Network (ANN) Analysis

Retrospective analysis of routinely collected clinical data using artificial neural network (ANN) algorithms to predict mortality and mechanical ventilation duration in ICU patients with COVID-19. No therapeutic intervention was applied to participants.

Sponsors & Collaborators

  • University of Gaziantep

    lead OTHER

Principal Investigators

  • Elzem Sen, Assoc Prof · University of Gaziantep

Eligibility

Min Age
18 Years
Max Age
98 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-02-01
Primary Completion
2025-02-01
Completion
2026-01-02

Countries

  • Turkey (Türkiye)

Study Locations

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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 NCT07436572 on ClinicalTrials.gov