Machine learning techniques for extracting relevant features from clinical data for COVID-19 mortality prediction

The role of Machine Learning (ML) in healthcare is based on the ability of a machine to analyse the huge amounts of data available for each patient, like age, medical history, overall health status, test results, etc. With ML algorithms it is possible to learn models from data for the early identification of pathologies and their severity. Early identification is crucial to proceed as soon as possible with the necessary therapeutic actions. This work applies modern ML techniques to clinical data of either COVID-19 positive and COVID-19 negative patients with pulmonary complications, to learn mortality prediction models for both groups of patients, and compare results. We have focused on symbolic methods for building classifiers able to extract patterns from clinical data. This approach leads to predictive Artificial Intelligence (AI) systems working on medical data, and able to explain the reasons that lead the systems themselves to reach a certain conclusion.