A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign
作者：
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.
摘要
•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.
论文关键词：COVID-19,Vaccination,Machine learning,XGBoost,Clinical decision support system,Model interpretability
论文评审过程：Received 31 March 2021, Revised 21 June 2021, Accepted 20 July 2021, Available online 22 July 2021, Version of Record 30 July 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108197
