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Related Experiment Video

Updated: Jul 1, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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

Panagiotis G Asteris¹, Amir H Gandomi², Danial J Armaghani³

¹Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.

European Journal of Internal Medicine

|March 8, 2024

Summary

Predicting COVID-19 intensive care unit (ICU) admission risk is crucial. A new algorithm using four basic blood tests achieved 97.12% accuracy in identifying high-risk patients, aiding clinical management.

Keywords:

Artificial intelligence COVID-19 Classification algorithms DERGA Genetic SARS-CoV2 hematological markers

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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.