@inproceedings{FraMazBiz21-ICTS4eHealth-IC,
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
Analysis},
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}
}
This file was generated by bibtex2html 1.98.