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Pregled bibliografske jedinice broj: 1195804

COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach


Blagojević, Anđela; Šušteršič, Tijana; Lorencin, Ivan; Baressi Šegota, Sandi; Milovanović, Dragan; Baskić, Danijela; Baskić, Dejan; Car, Zlatan; Filipović, Nenad; Zdravković, Nataša et al.
COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach // 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI)
Kragujevac, Srbija: Sveučilište u Kragujevcu, 2022. str. 1-4 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1195804 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach

Autori
Blagojević, Anđela ; Šušteršič, Tijana ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Milovanović, Dragan ; Baskić, Danijela ; Baskić, Dejan ; Car, Zlatan ; Filipović, Nenad ; Zdravković, Nataša ; Mijailović, Sara ; Zdravković, Nebojša

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI) / - Kragujevac, Srbija : Sveučilište u Kragujevcu, 2022, 1-4

Skup
1st Serbian International Conference on Applied Artificial Intelligence (SICAAI)

Mjesto i datum
Kragujevac, Srbija, 19-20.05.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
COVID-19 ; machine learning ; biomarkers ; modeling

Sažetak
The use of artificial intelligence, especially machine learning methods in creating models that will be applied in clinical practice has reached its peak with the appearance of the COVID-19 pandemic. This study aims to determine the severity of the clinical condition of COVID-19 patients based on blood marker analysis. The study used data from 60 COVID-19 patients treated at the Clinical Center Kragujevac. The research methodology includes the selection of the most important laboratory parameters as well as the classification of patients depending on them using methods of supervised learning, regression and classification. With an accuracy of 90%, three parameters were selected that can mostly indicate the severity of the patient's condition, which are: lactate dehydrogenase (LDH), C-reactive protein (CRP), white blood cells (WBC). Laboratory biomarkers such as LDH, CRP and WBC may have an impact on predicting outcomes and help classify patients into an appropriate group based on symptoms.

Izvorni jezik
Hrvatski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Temeljne medicinske znanosti, Kliničke medicinske znanosti



POVEZANOST RADA


Ustanove:
Tehnički fakultet, Rijeka

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada aai2022.kg.ac.rs

Citiraj ovu publikaciju:

Blagojević, Anđela; Šušteršič, Tijana; Lorencin, Ivan; Baressi Šegota, Sandi; Milovanović, Dragan; Baskić, Danijela; Baskić, Dejan; Car, Zlatan; Filipović, Nenad; Zdravković, Nataša et al.
COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach // 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI)
Kragujevac, Srbija: Sveučilište u Kragujevcu, 2022. str. 1-4 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Blagojević, A., Šušteršič, T., Lorencin, I., Baressi Šegota, S., Milovanović, D., Baskić, D., Baskić, D., Car, Z., Filipović, N. & Zdravković, N. (2022) COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach. U: 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI).
@article{article, author = {Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Lorencin, Ivan and Baressi \v{S}egota, Sandi and Milovanovi\'{c}, Dragan and Baski\'{c}, Danijela and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad and Zdravkovi\'{c}, Nata\v{s}a and Mijailovi\'{c}, Sara and Zdravkovi\'{c}, Neboj\v{s}a}, year = {2022}, pages = {1-4}, keywords = {COVID-19, machine learning, biomarkers, modeling}, title = {COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach}, keyword = {COVID-19, machine learning, biomarkers, modeling}, publisher = {Sveu\v{c}ili\v{s}te u Kragujevcu}, publisherplace = {Kragujevac, Srbija} }
@article{article, author = {Blagojevi\'{c}, An\djela and \v{S}u\v{s}ter\v{s}i\v{c}, Tijana and Lorencin, Ivan and Baressi \v{S}egota, Sandi and Milovanovi\'{c}, Dragan and Baski\'{c}, Danijela and Baski\'{c}, Dejan and Car, Zlatan and Filipovi\'{c}, Nenad and Zdravkovi\'{c}, Nata\v{s}a and Mijailovi\'{c}, Sara and Zdravkovi\'{c}, Neboj\v{s}a}, year = {2022}, pages = {1-4}, keywords = {COVID-19, machine learning, biomarkers, modeling}, title = {COVID-19 disease severity prediction model based on blood biomarkers: a machine learning approach}, keyword = {COVID-19, machine learning, biomarkers, modeling}, publisher = {Sveu\v{c}ili\v{s}te u Kragujevcu}, publisherplace = {Kragujevac, Srbija} }




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