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 theduration of mechanical ventilation and mortality in COVID-19 patients at the intensivecare unit.Methods: Data were collected from medical records of 113 COVID-19 patients who hadfollowed up at the intensive care unit between February 2020 and June 2020. An ANNapproach was used to predict the length of mechanical ventilation and mortality inCOVID-19 patients by evaluating patients' clinical data (demographic, laboratory, andcomorbidities).
Coronavirus disease 2019 (COVID-19) has led to an unprecedented burden on intensive care
units (ICUs), particularly due to high rates of respiratory failure requiring invasive
mechanical ventilation. Early identification of patients at risk for prolonged mechanical
ventilation and mortality is crucial for optimizing resource allocation and clinical
decision-making.
This retrospective cohort study aimed to develop and evaluate an artificial neural
network (ANN) model to predict mechanical ventilation duration and in-hospital mortality
among COVID-19 patients admitted to the ICU.
After approval by the Gaziantep University Clinical Research Ethics Committee (Decision
No: 2024/07, Date: 17.01.2024), data from 113 adult patients admitted to the ICU between
February 1, 2020 and June 30, 2020 were retrospectively analyzed. Demographic
characteristics, comorbidities, vital signs, laboratory parameters, severity scores
(e.g., APACHE, SOFA), treatment modalities, and clinical outcomes were extracted from
medical records.
Artificial neural network models were developed using commercially available software
(Alyuda NeuroIntelligence, Alyuda Research Inc., Los Altos, CA, USA). Multiple training
algorithms, including Quick Propagation, Conjugate Gradient Descent, Limited Memory
Quasi-Newton, Online Backpropagation, and Batch Backpropagation, were tested. Model
performance was evaluated using 10-fold cross-validation. Predictive accuracy for
mortality and correlation performance for mechanical ventilation duration were
calculated. Classical statistical methods, including multiple linear regression and
binary logistic regression, were also performed for comparison.
The primary objective was to assess the predictive performance of ANN models for ICU
mortality. A secondary objective was to evaluate ANN performance in estimating mechanical
ventilation duration. This study was conducted in accordance with the Declaration of
Helsinki.
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.
Inclusion Criteria:
Age ≥ 18 years
Confirmed diagnosis of COVID-19
Admission to the intensive care unit (ICU) between February 1, 2020 and June 30, 2020
Availability of complete clinical, laboratory, and outcome data in medical records
Exclusion Criteria:
Age < 18 years
Incomplete or missing clinical data
Transfer to another institution before outcome assessment
Readmission to ICU during the same hospitalization (only first admission included)
Gaziantep University Hospital
Gaziantep, Turkey (Türkiye)
Elzem Sen, Assoc Prof, Principal Investigator
University of Gaziantep