Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.
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
- Publisher:
- ELSEVIER
- Publication Type:
- Journal Article
- Citation:
- Eur J Intern Med, 2024, 125, pp. 67-73
- Issue Date:
- 2024-07
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Full metadata record
Field | Value | Language |
---|---|---|
dc.contributor.author | Asteris, PG | |
dc.contributor.author | Gandomi, AH | |
dc.contributor.author | Armaghani, DJ | |
dc.contributor.author | Kokoris, S | |
dc.contributor.author | Papandreadi, AT | |
dc.contributor.author | Roumelioti, A | |
dc.contributor.author | Papanikolaou, S | |
dc.contributor.author | Tsoukalas, MZ | |
dc.contributor.author | Triantafyllidis, L | |
dc.contributor.author | Koutras, EI | |
dc.contributor.author | Bardhan, A | |
dc.contributor.author | Mohammed, AS | |
dc.contributor.author | Naderpour, H | |
dc.contributor.author | Paudel, S | |
dc.contributor.author | Samui, P | |
dc.contributor.author | Ntanasis-Stathopoulos, I | |
dc.contributor.author | Dimopoulos, MA | |
dc.contributor.author | Terpos, E | |
dc.date.accessioned | 2025-03-17T05:19:45Z | |
dc.date.available | 2024-02-29 | |
dc.date.available | 2025-03-17T05:19:45Z | |
dc.date.issued | 2024-07 | |
dc.identifier.citation | Eur J Intern Med, 2024, 125, pp. 67-73 | |
dc.identifier.issn | 0953-6205 | |
dc.identifier.issn | 1879-0828 | |
dc.identifier.uri | http://hdl.handle.net/10453/185903 | |
dc.description.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. | |
dc.format | Print-Electronic | |
dc.language | eng | |
dc.publisher | ELSEVIER | |
dc.relation.ispartof | Eur J Intern Med | |
dc.relation.isbasedon | 10.1016/j.ejim.2024.02.037 | |
dc.rights | info:eu-repo/semantics/restrictedAccess | |
dc.subject | 1103 Clinical Sciences | |
dc.subject.classification | General & Internal Medicine | |
dc.subject.classification | 3201 Cardiovascular medicine and haematology | |
dc.subject.classification | 3202 Clinical sciences | |
dc.subject.mesh | Humans | |
dc.subject.mesh | COVID-19 | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Male | |
dc.subject.mesh | Female | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Intensive Care Units | |
dc.subject.mesh | Prognosis | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Severity of Illness Index | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | SARS-CoV-2 | |
dc.subject.mesh | Ferritins | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Neutrophils | |
dc.subject.mesh | Adult | |
dc.subject.mesh | L-Lactate Dehydrogenase | |
dc.subject.mesh | Neutrophils | |
dc.subject.mesh | Humans | |
dc.subject.mesh | L-Lactate Dehydrogenase | |
dc.subject.mesh | Prognosis | |
dc.subject.mesh | Severity of Illness Index | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | Adult | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Intensive Care Units | |
dc.subject.mesh | Female | |
dc.subject.mesh | Male | |
dc.subject.mesh | Ferritins | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | COVID-19 | |
dc.subject.mesh | SARS-CoV-2 | |
dc.subject.mesh | Humans | |
dc.subject.mesh | COVID-19 | |
dc.subject.mesh | Machine Learning | |
dc.subject.mesh | Male | |
dc.subject.mesh | Female | |
dc.subject.mesh | Middle Aged | |
dc.subject.mesh | Intensive Care Units | |
dc.subject.mesh | Prognosis | |
dc.subject.mesh | Aged | |
dc.subject.mesh | Severity of Illness Index | |
dc.subject.mesh | Algorithms | |
dc.subject.mesh | SARS-CoV-2 | |
dc.subject.mesh | Ferritins | |
dc.subject.mesh | Neural Networks, Computer | |
dc.subject.mesh | Neutrophils | |
dc.subject.mesh | Adult | |
dc.subject.mesh | L-Lactate Dehydrogenase | |
dc.title | Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm. | |
dc.type | Journal Article | |
utslib.citation.volume | 125 | |
utslib.location.activity | Netherlands | |
utslib.for | 1103 Clinical Sciences | |
pubs.organisational-group | University of Technology Sydney | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology | |
pubs.organisational-group | University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering | |
pubs.organisational-group | University of Technology Sydney/UTS Groups | |
pubs.organisational-group | University of Technology Sydney/UTS Groups/Data Science Institute (DSI) | |
utslib.copyright.status | in_progress | * |
dc.date.updated | 2025-03-17T05:19:41Z | |
pubs.publication-status | Published | |
pubs.volume | 125 |
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.
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