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

Resultado final

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.

Publicaciones

Peer reviewed articles (1)

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

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

Conference proceedings (5)

Utilizing Domain Knowledge in End-to-End Audio Processing

Autores: Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe
Publicado en: 31st Conference on Neural Information Processing Systems (NIPS 2017), 2017

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

Autores: Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
Publicado en: arXiv preprint arXiv:1902.02102, 2019

Exploiting Nontrivial Connectivity for Automatic Speech Recognition

Autores: Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax, Marco Singh, Lars Maaløe
Publicado en: NIPS workshop on machine learning for audio, 2017

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

Autores: Jan Kremer, Corti, Copenhagen, Denmark, [e-mail] Lasse Borgholt, Corti, Copenhagen, Denmark, [e-mail] Lars Maaløe , Corti, Copenhagen, Denmark, [e-mail]
Publicado en: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), 2018

Towards Hierarchical Discrete Variational Autoencoders

Autores: Valentin Liévin, Andrea Dittadi, Lars Maaløe, Ole Winther
Publicado en: NeurIPS Workshop on Advances in Approximate Bayesian Inference, 2019

Other (1)

Detecting Out-of-Hospital Cardiac Arrest Using Artificial Intelligence

Autores: Andreas Cleve, Dimitri Devillers, Matteo Palladini, Jerome Paris, Rose Michael, Etienne Faure, Rodolfo Bonora
Publicado en: 2020