author = {Michele Fraccaroli and Giulia Mazzucchelli and Alice Bizzarri},
  title = {Machine Learning Techniques for Extracting Relevant Features from Clinical
	Data for {COVID-19} Mortality Prediction},
  booktitle = {2021 Symposium on Computers and Communications (ISCC): 26th IEEE Symposium
	on Computers and Communications - Workshop on ICT Solutions for eHealth
	(ICTS4eHealth) (ICTS4eHealth2021)},
  address = {Athens, Greece},
  days = 4,
  month = sep,
  year = 2021,
  keywords = {Machine Learning; COVID-19; Health Informatics; Explanation; Predictive
  abstract = {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.},
  pages = {1-7},
  doi = {10.1109/ISCC53001.2021.9631477}

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