Abstract
Proper triage of COVID-19 patients is a key factor in effective case management, especially with limited and insufficient 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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Klaudel, B., Obuchowski, A., Karski, R., Rydziński, B., Jasik, P., Kowalczuk, Z. (2023). COVID-19 Severity Forecast Based on Machine Learning and Complete Blood Count Data. In: Kowalczuk, Z. (eds) Intelligent and Safe Computer Systems in Control and Diagnostics. DPS 2022. Lecture Notes in Networks and Systems, vol 545. Springer, Cham. https://doi.org/10.1007/978-3-031-16159-9_5
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