Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data
Lorenzo Famiglini, Giorgio Bini, Anna Carobene, Andrea Campagner, Federico Cabitza
Abstract
In this article we discuss the development of prognostic Machine Learning (ML) models for COVID-19 progression: specifically, we address the task of predicting intensive care unit (ICU) admission in the next 5 days. We developed three ML models on the basis of 4995 Complete Blood Count (CBC) tests. We propose three ML models that differ in terms of interpretability: two fully interpretable models and a black-box one. We report an AUC of. 81 and. 83 for the interpretable models (the decision tree and logistic regression, respectively), and an AUC of. 88 for the black-box model (an ensemble). This shows that CBC data and ML methods can be used for cost-effective prediction of ICU admission of COVID-19 patients: in particular, as the CBC can be acquired rapidly through routine blood exams, our models could also be applied in resource-limited settings and to get fast indications at triage and daily rounds.