Naslov
Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models

Autori
Baressi Šegota, Sandi ; Lorencin, Ivan ; Anđelić, Nikola ; Štifanić, Daniel ; Musulin, Jelena ; Vlahinić, Saša ; Šušteršič, Tijana ; Blagojević, Anđela ; Car, Zlatan

Izvornik
EAI Endorsed Transactions on Bioengineering and Bioinformatics (2709-4111) 21 (2021), 3; E2, 9

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
artificial intelligence ; bio-engineering ; bio-inspired systems ; bio-inspired models ; COVID-19 ; epidemiology curves ; machine learning ; multilayer perceptron

Sažetak
INTRODUCTION: The development of epidemiological curve models is one of the key factors in the combat of epidemiological diseases such as COVID-19. OBJECTIVES: The goal of this paper is to develop a system for automatic training and testing of AI-based regressive models of epidemiological curves using public data, which involves automating the data acquisition and speeding up the training of the models. METHODS: The research applies Multilayer Perceptron (MLP) for the creation of models, implemented within a system for automatic data fetching and training, and evaluated using the coefficient of determination (R2). Training time is lowered through the application of data filtering and simplifying the model selection. RESULTS: The developed system can train high precision models rapidly, allowing for quick model delivery All trained models achieve scores which are higher than 0.95. CONCLUSION: The results show that the development of a quick COVID-19 spread modeling system is possible.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti, Javno zdravstvo i zdravstvena zaštita, Biotehnologija

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