Artificial Intelligence for Emergency Medical Services: a smart digital assistant for faster and more accurate cardiac arrest recognition during emergency calls

Project Information

AI4EMS

Grant agreement ID: 823383

DOI 10.3030/823383

Project closed

EC signature date 20 August 2018

Start date 1 August 2018

End date 31 January 2020

Funded under

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Total cost € 2 055 976,25

EU contribution € 1 439 183,00

Coordinated by

CORTI APS
Denmark

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Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Websites, patent fillings, videos etc. (1)

AI4EMS online content

AI4EMS online content. The project content will be divide in a public section and a restricted area (accessible only to the project participants and the EC officers allocated to the project) for uploading and downloading of all of the project reports and documents.

Publications

Peer reviewed articles (1)

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

Author(s): Stig Nikolaj Blomberg, Fredrik Folke, Annette Kjær Ersbøll, Helle Collatz Christensen, Christian Torp-Pedersen, Michael R. Sayre, Catherine R. Counts, Freddy K. Lippert
Published in: Resuscitation, Issue 138, 2019, Page(s) 322-329, ISSN 0300-9572
Publisher: Elsevier BV
DOI: 10.1016/j.resuscitation.2019.01.015

Conference proceedings (5)

Utilizing Domain Knowledge in End-to-End Audio Processing

Author(s): Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe
Published in: 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017
Publisher: NIPS

BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

Author(s): Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
Published in: arXiv preprint arXiv:1902.02102, 2019
Publisher: arXiv

Exploiting Nontrivial Connectivity for Automatic Speech Recognition

Author(s): Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax, Marco Singh, Lars Maaløe
Published in: NIPS workshop on machine learning for audio, 2017
Publisher: NIPS

On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition

Author(s): Jan Kremer, Corti, Copenhagen, Denmark, [e-mail] Lasse Borgholt, Corti, Copenhagen, Denmark, [e-mail] Lars Maaløe , Corti, Copenhagen, Denmark, [e-mail]
Published in: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018
Publisher: NIPS

Towards Hierarchical Discrete Variational Autoencoders

Author(s): Valentin Liévin, Andrea Dittadi, Lars Maaløe, Ole Winther
Published in: NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019
Publisher: OpenReview

Other (1)

Detecting Out-of-Hospital Cardiac Arrest Using Artificial Intelligence

Author(s): Andreas Cleve, Dimitri Devillers, Matteo Palladini, Jerome Paris, Rose Michael, Etienne Faure, Rodolfo Bonora
Published in: 2020
Publisher: European Emergency Number Association