MEDIWARN addresses the problem of delayed early medical intervention in critically ill patients

The project proposes a technologically advanced system capable of providing high standards of care for hospitalized patients capitalizing on the knowledge deriving from the method EWS (Early Warning Score).

Expected Results:

5 enterprises operating in the electro-medical sector that will acquire the technology developed within the project

The project realizes an innovative monitoring system that allows the acquisition in real time of the vital parameters of the patients using a peripheral sensory system

Project activities will realize:

23 virtual biosensor

5 cross-border research institutes and enterprises involved in actions aimed at industrial prototyping and marketing of technological products deriving from the project

In order to implement actions to combat, contain and adapt to the consequences of the COVID-19 epidemic, the Mediwarn project aims to adapt the innovative monitoring system with fuzzy logic to additional "COVID oriented activities

Within the project, the partner AOU Policlinico V. Emanuele will collect the physiological parameters of patients who tested positive for Covid-19. The main physiological signals monitored will already be identified in the first part of the project, with the integration of any parameters useful to provide more details on the patient's health status.

Once completed this first phase, an analysis of the data collected will be made in order to identify which parameters determine a degeneration of the patient's clinical status. These activities will provide an improvement to the fuzzy logic implemented in the previous project WPs and will assist medical and nursing staff in providing better assistance to patients with Covid-19, as well as identifying an early degeneration of their health.

The output of those additional actions is a summary document that will describe the physiological parameters analyzed in the monitoring phase of Covid-19 patients and identifying the main precursors of medical alert. The algorithm developed in fuzzy logic will provide results that are more accurate and this taking in consideration the integrations from coronavirus patients.

Project Lead Partner:

Università degli Studi di Catania - Dipartimento di Ingegneria Civile e Architettura

Project Partners:

University of Malta - Faculty of Medicine and Surgery, Department of Surgery

Azienda Ospedaliero - Universitaria “Policlinico - Vittorio Emanuele” U.O.C. Anestesia e Rianimazione 2

Email

rettore@unipa.it

Website

https://mediwarn.net/

Facebook

https://www.facebook.com/Mediwarn/