Abstract
One of the most important challenges in the scenario of COVID-19 is to design and develop decision support systems that can help medical staff to identify a cohort of patients that is more likely to have worse clinical evolution. To achieve this objective it is necessary to work on collected data, pre-process them in order to obtain a consistent dataset and then extract the most relevant features with advanced statistical methods like principal component analysis. As preliminary results of this research, very influential features that emerged are the presence of cardiac and liver illnesses and the levels of some inflammatory parameters at the moment of diagnosis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
