Search
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.
Citations
-
2
CrossRef
-
0
Web of Science
-
0
Scopus
Authors (6)
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Monographic publication
- Type:
- rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
- 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// Intelligent and Safe Computer Systems in Control and Diagnostics/ : , , s.52-62
- DOI:
- Digital Object Identifier (open in new tab) 10.1007/978-3-031-16159-9_5
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
seen 145 times