{Reference Type}: Journal Article
{Title}: Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.
{Author}: Asteris PG;Gandomi AH;Armaghani DJ;Kokoris S;Papandreadi AT;Roumelioti A;Papanikolaou S;Tsoukalas MZ;Triantafyllidis L;Koutras EI;Bardhan A;Mohammed AS;Naderpour H;Paudel S;Samui P;Ntanasis-Stathopoulos I;Dimopoulos MA;Terpos E;
{Journal}: Eur J Intern Med
{Volume}: 125
{Issue}: 0
{Year}: 2024 Jul 7
{Factor}: 7.749
{DOI}: 10.1016/j.ejim.2024.02.037
{Abstract}: It 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.