Pregled bibliografske jedinice broj: 1077467
Comparison Of Three Artificial Intelligence Algorithms For Sepsis Prediction
Comparison Of Three Artificial Intelligence Algorithms For Sepsis Prediction // World of Health, 3 (2020), 20-25 (domaća recenzija, članak, ostalo)
CROSBI ID: 1077467 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Comparison Of Three Artificial Intelligence
Algorithms For Sepsis Prediction
Autori
Jelena Musulin, Daniel Štifanić, Ivan Lorencin, Sandi Baressi Šegota, Nikola Anđelić1, Emanuel Borović, Alen Protić, Zlatan Car
Izvornik
World of Health (2623-5773) 3
(2020);
20-25
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, ostalo
Ključne riječi
Sepsis, Artificial Neural Networks, Support Vector Machines, K-Nearest Neigh-bors
Sažetak
Sepsis is the most severe consequence of bac- terial infections, in other words, a potentially life-threatening condition. During infection, some components of the innate immune response can, under certain circumstances, cause multiple organ dysfunction and tissue damage. Fortunately, timely treatment can prevent the consequences. The algorithms used to solve the problem of predicting sep-sis occurrence are Support-Vector Machine (SVM), K- Nearest Neighbors (KNN) and Ar-tificial Neural Network (ANN). The dataset used for this research consists of 23 different medical parameters for each patient, which is the total of 2277 data-points. For each of the aforementioned algorithms, different combi- nations of parameters were examined as well as different types of kernel functions (SVM), architectures (ANN), and number of nearest neighbors (KNN). The experimental results showed that the ANN approach achieves the highest AUC value (0.992) compared to the other approaches like SVM and KNN. (PDF) Comparison of Three Artificial Intelligence Algorithms for Sepsis Prediction. Available from: https://www.researchgate.net/publication/344152 372_Comparison_of_Three_Artificial_Intelligence _Algorithms_for_Sepsis_Prediction [accessed Sep 07 2020].
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Kliničke medicinske znanosti, Javno zdravstvo i zdravstvena zaštita, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje)
POVEZANOST RADA
Projekti:
Ostalo-CEI - 305.6019-20 - Use of regressive artificial intelligence (AI) and machine learning (ML) methods in modelling of COVID-19 spread (COVIDAi) (Car, Zlatan, Ostalo - CEI Extraordinary Call for Proposals 2020) ( CroRIS)
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)
--KK.01.1.1.01.009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (DATACROSS) (Šmuc, Tomislav; Lončarić, Sven; Petrović, Ivan; Jokić, Andrej; Palunko, Ivana) ( CroRIS)
CIII-HR-0108
KK.01.2.2.03.0004
uniri-tehnic-18-275-1447
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
Ustanove:
Medicinski fakultet, Rijeka,
Tehnički fakultet, Rijeka,
Sveučilište u Rijeci - Odjel za biotehnologiju
