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

Información del proyecto

ENVISION

Identificador del acuerdo de subvención: 101015930

DOI 10.3030/101015930

Proyecto cerrado

Fecha de la firma de la CE 13 Octubre 2020

Fecha de inicio 1 Diciembre 2020

Fecha de finalización 31 Julio 2023

Financiado con arreglo a

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

Coste total € 5 660 486,25

Aportación de la UE € 4 874 111,25

Coordinado por

JOHANN WOLFGANG GOETHE-UNIVERSITAET FRANKFURT AM MAIN
Germany

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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

Publicaciones

Peer reviewed articles (3)

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

Autores: 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
Publicado en: Critical Care, Edición 295 (2021), 2021, Página(s) 25, ISSN 1364-8535
Editor: BMC
DOI: 10.1186/s13054-021-03720-4

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

Autores: Alpo Olavi Värri, Antti Kallonen, Hannu Nieminen, Mark Van Gils
Publicado en: Finnish Journal of EHealth and EWelfare, Edición 13 (4), 2021, Página(s) 449-453, ISSN 1798-0798
Editor: 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

Autores: 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
Publicado en: Springer Nature, 2021, ISSN 1364-8535
Editor: BMC
DOI: 10.6084/m9.figshare.15184814