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.

目前的 ML 方法并没有完全专注于回答一个尚未解决的热门挑战，即预测 COVID-19 疫苗管理的优先级。因此，我们的任务包括一些额外的方法学挑战，主要与在处理具有高度不平衡性质的分类和有序数据时避免不必要的偏见有关。因此，本研究的主要贡献是提出了一种机器学习算法，即使用包含 17k 患者电子健康记录数据的意大利全科医生联合会数据集对 COVID-19 疫苗管理进行优先分类的分层优先级分类 eXtreme Gradient Boosting。我们测量了所提出的方法对所有优先级进行分类的有效性，同时展示了相对于现有技术的显着改进。拟议的 ML 方法已集成到临床决策支持系统中，目前正在支持全科医生将 COVID-19 疫苗管理优先级分配给他们的助手。