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

Livrables

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

ENVISION website

ENVISION website with dedicated areas for different stakeholder groups

Publications

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

Auteurs: 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
Publié dans: Critical Care, Numéro 295 (2021), 2021, Page(s) 25, ISSN 1364-8535
Éditeur: BMC
DOI: 10.1186/s13054-021-03720-4

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

Auteurs: Alpo Olavi Värri, Antti Kallonen, Hannu Nieminen, Mark Van Gils
Publié dans: Finnish Journal of EHealth and EWelfare, Numéro 13 (4), 2021, Page(s) 449-453, ISSN 1798-0798
Éditeur: 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

Auteurs: 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
Publié dans: Springer Nature, 2021, ISSN 1364-8535
Éditeur: BMC
DOI: 10.6084/m9.figshare.15184814

Livrables

Publications

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