ABSTRACT
In 2020, severe coronavirus 2 respiratory syndrome (SARS-Cov-2) has quickly risen, becoming a worldwide pandemic that is still ongoing nowadays. Differently from other viruses the COVID-19, responsible for SARS-Cov-2, demonstrated an unmatched capability of transmission that led towards an unprecedented challenge for the global health system. All health facilities, ranging from Hospitals to local health surveillance units, have been severely tested due to the high number of infected people. In this scenario, the use of methodologies that can improve and optimize, at any level, the management of infected patients is highly advisable. One of the goals of Artificial Intelligence in medicine is to develop advanced tools and methodologies to support patient care and to help physicians and medical work in the decision-making process. More specifically, Machine Learning (ML) methods have been successfully used to build predictive models starting from clinical patient data. In our paper, we study whether ML can be used to build prognostic models capable of predicting the potential disease outcome. In our study, we evaluate different unsupervised and supervised ML approaches using SARS-Cov-2 data collected from the "Azienda Ospedaliera SS Antonio e Biagio e Cesare Arrigo"Hospital in Alessandria area, Italy, from 24th February to 31st October 2020. Our preliminary goal is to develop a ML model able to promptly identify patients with a high risk of fatal outcome, to steer medical doctors and clinicians towards the best management strategies. © 2021 IEEE.