Intelligent plug-and-play digital tool for real-time surveillance of COVID-19 patients and smart decision-making in Intensive Care Units

Informazioni relative al progetto

ENVISION

ID dell’accordo di sovvenzione: 101015930

DOI 10.3030/101015930

Progetto chiuso

Data della firma CE 13 Ottobre 2020

Data di avvio 1 Dicembre 2020

Data di completamento 31 Luglio 2023

Finanziato da

• SOCIETAL CHALLENGES - Health, demographic change and well-being

Costo totale € 5 660 486,25

Contributo UE € 4 874 111,25

Coordinato da

JOHANN WOLFGANG GOETHE-UNIVERSITAET FRANKFURT AM MAIN
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (8)

Governance report

Report specifying decisionmaking processes and appointed members on projectinternal bodies and team as well as signed charters for all external monitoring boards and committees

Assessment plan
Guidelines on ethical, social, legal and psychological aspects of ENVISION

Analysis of legal social and psychological aspects related to the project

Outreach plan

Outreach plan defining strategy tools channels dissemination and communication activities

Sandman.ICU user guide and training material

SandmanICU user guide and training material

eLearning materials

eLearning materials developed specifically for health care professionals

Health economic model

Health economic model including report and webtool for countryor region specific estimates

Final event

Final event to present the results and outcomes in the European Parliament

Websites, patent fillings, videos etc. (1)

ENVISION website

ENVISION website with dedicated areas for different stakeholder groups

Pubblicazioni

Peer reviewed articles (3)

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Autori: Harry Magunia, Simone Lederer, Raphael Verbuecheln, Bryant Joseph Gilot, Michael Koeppen, Helene A. Haeberle, Valbona Mirakaj, Pascal Hofmann, Gernot Marx, Johannes Bickenbach, Boris Nohe, Michael Lay, Claudia Spies, Andreas Edel, Fridtjof Schiefenhövel, Tim Rahmel, Christian Putensen, Timur Sellmann, Thea Koch, Timo Brandenburger, Detlef Kindgen-Milles, Thorsten Brenner, Marc Berger, Kai Zacharo
Pubblicato in: Critical Care, Numero 295 (2021), 2021, Pagina/e 25, ISSN 1364-8535
Editore: BMC
DOI: 10.1186/s13054-021-03720-4

ENVISION – Improve intensive care of COVID-19 patients with artificial intelligence

Autori: Alpo Olavi Värri, Antti Kallonen, Hannu Nieminen, Mark Van Gils
Pubblicato in: Finnish Journal of EHealth and EWelfare, Numero 13 (4), 2021, Pagina/e 449-453, ISSN 1798-0798
Editore: Finnish Social and Health Informatics Association
DOI: 10.23996/fjhw.109929

Additional file 1 of Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Autori: Magunia, Harry; Lederer, Simone; Verbuecheln, Raphael; Gilot, Bryant Joseph; Koeppen, Michael; Haeberle, Helene A.; Mirakaj, Valbona; Hofmann, Pascal; Marx, Gernot; Bickenbach, Johannes; Nohe, Boris; Lay, Michael; Spies, Claudia; Edel, Andreas; Schiefenhövel, Fridtjof; Rahmel, Tim; Putensen, Christian; Sellmann, Timur; Koch, Thea; Brandenburger, Timo; Kindgen-Milles, Detlef; Brenner, Thorsten; Be
Pubblicato in: Springer Nature, 2021, ISSN 1364-8535
Editore: BMC
DOI: 10.6084/m9.figshare.15184814