• Panagiotis G Asteris, Amir H Gandomi, Danial J Armaghani, Styliani Kokoris, Anastasia T Papandreadi, Anna Roumelioti, Stefanos Papanikolaou, Markos Z Tsoukalas, Leonidas Triantafyllidis, Evangelos I Koutras, Abidhan Bardhan, Ahmed Salih Mohammed, Hosein Naderpour, Satish Paudel, Pijush Samui, Ioannis Ntanasis-Stathopoulos, Meletios A Dimopoulos, and Evangelos Terpos.
• Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.
• Eur. J. Intern. Med. 2024 Jul 1; 125: 677367-73.

AbstractIt is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms. The optimal combination of hematological indicators that gives the best prediction consists of only four hematological indicators as follows: neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase, ferritin, and albumin. The best prediction corresponds to a particularly high accuracy of 97.12 %. In conclusion, our novel approach provides a robust model based only on basic hematological parameters for predicting the risk for ICU admission and optimize COVID-19 patient management in the clinical practice.Copyright © 2024 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

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