A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign. (January 2022) - Romeo, Luca; Frontoni, Emanuele
Highlights: A unified Hierachical XGBoost model for classifying priorities for COVID-19 vaccination campaign. Avoiding bias in the learning procedure. Experimental results on a novel FIMMG_COVID EHR dataset. High predictive performances and model interpretability. Integration in a CDSS for supporting the GPs for assigning COVID-19 vaccine administration priorities. Abstract: The current ML approaches do not fully focus to answer a still unresolved and topical challenge, namely the prediction of priorities of COVID-19 vaccine administration. Thus, our task includes some additional methodological challenges mainly related to avoiding unwanted bias while handling categorical and ordinal data with a highly imbalanced nature. Hence, the main contribution of this study is to propose a machine learning algorithm, namely Hierarchical Priority Classification eXtreme Gradient Boosting for priority classification for COVID-19 vaccine administration using the Italian Federation of General Practitioners dataset that contains Electronic Health Record data of 17k patients. We measured the effectiveness of the proposed methodology for classifying all the priority classes while demonstrating a significant improvement with respect to the state of the art. The proposed ML approach, which is integrated into a clinical decision support system, is currently supporting General Pracitioners in assigning COVID-19 vaccine administration priorities to their assistants.