Prediction of ICU admission for COVID-19 patients: a Machine Learning approach based on Complete Blood Count data

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.

Automated summary

The research team developed three machine learning models based on a large number of complete blood count tests, including a decision tree, a logistic regression, and a black-box model, to predict whether COVID-19 patients will need to be transferred to the ICU in the next 5 days. The results showed that the black-box model had the highest AUC of 0.88. Therefore, this study demonstrates that CBC data and machine learning methods can be effectively used to predict the need for ICU admission in COVID-19 patients, particularly in resource-limited environments.