Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

Area of Science:

• Medical informatics
• Hematology
• Infectious diseases

Background:

• Accurate prediction of COVID-19 intensive care unit (ICU) admission risk is vital for emergency department patient management.
• Existing models may require complex data, hindering rapid clinical application.

Purpose of the Study:

• To develop and validate a novel predictive model for ICU admission in COVID-19 patients.
• To identify key hematological indices for accurate risk stratification.

Main Methods:

• Utilized artificial neural networks and a Data Ensemble Refinement Greedy Algorithm (DERGA).
• Trained and tested the model on a database of 1596 COVID-19 patients.
• Evaluated model performance using 15 accessible hematological indices.

Main Results:

• Identified an optimal combination of four hematological indicators: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin.
• Achieved a high prediction accuracy of 97.12% for ICU admission risk.
• Demonstrated the model's reliability on a separate testing dataset.

Conclusions:

• The novel DERGA model provides a robust and accurate method for predicting COVID-19 ICU admission risk.
• The model relies solely on easily accessible basic hematological parameters.
• This approach can optimize clinical decision-making and patient management for COVID-19.