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

Informazioni relative al progetto

AI4EMS

ID dell’accordo di sovvenzione: 823383

DOI 10.3030/823383

Progetto chiuso

Data della firma CE 20 Agosto 2018

Data di avvio 1 Agosto 2018

Data di completamento 31 Gennaio 2020

Finanziato da

• INDUSTRIAL LEADERSHIP - Innovation In SMEs

Costo totale € 2 055 976,25

Contributo UE € 1 439 183,00

Coordinato da

CORTI APS
Denmark

Risultati finali

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.

Pubblicazioni

Peer reviewed articles (1)

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

Autori: Stig Nikolaj Blomberg, Fredrik Folke, Annette Kjær Ersbøll, Helle Collatz Christensen, Christian Torp-Pedersen, Michael R. Sayre, Catherine R. Counts, Freddy K. Lippert
Pubblicato in: Resuscitation, Numero 138, 2019, Pagina/e 322-329, ISSN 0300-9572
Editore: Elsevier BV
DOI: 10.1016/j.resuscitation.2019.01.015

Conference proceedings (5)

Utilizing Domain Knowledge in End-to-End Audio Processing

Autori: Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe
Pubblicato in: 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017
Editore: NIPS

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

Autori: Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
Pubblicato in: arXiv preprint arXiv:1902.02102, 2019
Editore: arXiv

Exploiting Nontrivial Connectivity for Automatic Speech Recognition

Autori: Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax, Marco Singh, Lars Maaløe
Pubblicato in: NIPS workshop on machine learning for audio, 2017
Editore: NIPS

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

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

Towards Hierarchical Discrete Variational Autoencoders

Autori: Valentin Liévin, Andrea Dittadi, Lars Maaløe, Ole Winther
Pubblicato in: NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019
Editore: OpenReview

Other (1)

Detecting Out-of-Hospital Cardiac Arrest Using Artificial Intelligence

Autori: Andreas Cleve, Dimitri Devillers, Matteo Palladini, Jerome Paris, Rose Michael, Etienne Faure, Rodolfo Bonora
Pubblicato in: 2020
Editore: European Emergency Number Association