COVID-19 severity forecast based on machine learning and complete blood count data

Barbara Klaudel; Aleksander Obuchowski; Roman Karski; Bartosz Rydziński; Patryk Jasik; Zdzisław Kowalczuk

COVID-19 severity forecast based on machine learning and complete blood count data

Abstract

Proper triage of COVID-19 patients is a key factor in eective case management, especially with limited and insucient resources. In this paper, we propose a machine-aided diagnostic system to predict how badly a patient with COVID-19 will develop disease. The prognosis of this type is based on the parameters of commonly used complete blood count tests, which makes it possible to obtain data from a wide range of patients.We chose the four-tier nursing care category as the outcome variable. In this paper, we compare traditional tree-based machine learning models with approaches based on neural networks. The developed tool achieves a weighted average F1 score of 73% for a three-class COVID-19 severity forecast. We show that the complete blood count test can form the basis of a convenient and easily accessible method of predicting COVID-19 severity. Of course, such a model requires meticulous validation before it is proposed for inclusion in real medical procedures.

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Category:: Conference activity
Type:: publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
Language:: English
Publication year:: 2022
Bibliographic description:: Klaudel B., Obuchowski A., Karski R., Rydziński B., Jasik P., Kowalczuk Z.: COVID-19 severity forecast based on machine learning and complete blood count data// / : , 2022,
Verified by:: Gdańsk University of Technology