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Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression (CROSBI ID 298536)

Prilog u časopisu | ostalo | međunarodna recenzija

Blagojević, Anđela ; Šušteršič, Tijana ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Anđelić, Nikola ; Milovanović, Dragan ; Baskić, Danijela ; Baskić, Dejan ; Petrović Zdravković, Nataša ; Sazdanović, Predrag et al. Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression // Computers in biology and medicine, 138 (2021), 104869, 11. doi: 10.1016/j.compbiomed.2021.104869

Podaci o odgovornosti

Blagojević, Anđela ; Šušteršič, Tijana ; Lorencin, Ivan ; Baressi Šegota, Sandi ; Anđelić, Nikola ; Milovanović, Dragan ; Baskić, Danijela ; Baskić, Dejan ; Petrović Zdravković, Nataša ; Sazdanović, Predrag ; Car, Zlatan ; Filipović, Nenad

engleski

Artificial intelligence approach towards assessment of condition of COVID-19 patients - Identification of predictive biomarkers associated with severity of clinical condition and disease progression

Background and objectives: Although ML has been studied for different epidemiological and clinical issues as well as for survival prediction of COVID-19, there is a noticeable shortage of literature dealing with ML usage in prediction of disease severity changes through the course of the disease. In that way, predicting disease progression from mild towards moderate, severe and critical condition, would help not only to respond in a timely manner to prevent lethal results, but also to minimize the number of patients in hospitals where this is not necessary. Methods: We present a methodology for the classification of patients into 4 distinct categories of the clinical condition of COVID-19 disease. Classification of patients is based on the values of blood biomarkers that were assessed by Gradient boosting regressor and which were selected as biomarkers that have the greatest influence in the classification of patients with COVID- 19. Results: The results show that among several tested algorithms, XGBoost classifier achieved best results with an average accuracy of 94% and an average F1-score of 94.3%. We have also extracted 10 best features from blood analysis that are strongly associated with patient condition and based on those features we can predict the severity of the clinical condition. Conclusions: The main advantage of our system is that it is a decision tree-based algorithm which is easier to interpret, instead of the use of black box models, which are not appealing in medical practice.

COVID-19 ; clinical condition assessment ; predictive blood biomarkers ; rule-based machine learning ; personalized model

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Podaci o izdanju

138

2021.

104869

11

objavljeno

0010-4825

1879-0534

10.1016/j.compbiomed.2021.104869

Trošak objave rada u otvorenom pristupu

Povezanost rada

Elektrotehnika, Javno zdravstvo i zdravstvena zaštita, Računarstvo, Temeljne medicinske znanosti, Temeljne tehničke znanosti

Poveznice
Indeksiranost