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AI4EMS SIGNED

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

Total Cost €

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EC-Contrib. €

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Partnership

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 AI4EMS project word cloud

Explore the words cloud of the AI4EMS project. It provides you a very rough idea of what is the project "AI4EMS" about.

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Project "AI4EMS" data sheet

The following table provides information about the project.

Coordinator
CORTI APS 

Organization address
address: BLEGDAMSVEJ 6
city: KOBENHAVN
postcode: 2200
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Denmark [DK]
 Project website https://corti.ai/
 Total cost 2˙055˙976 €
 EC max contribution 1˙439˙183 € (70%)
 Programme 1. H2020-EU.3. (PRIORITY 'Societal challenges)
2. H2020-EU.2.3. (INDUSTRIAL LEADERSHIP - Innovation In SMEs)
3. H2020-EU.2.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies)
 Code Call H2020-SMEInst-2018-2020-2
 Funding Scheme SME-2
 Starting year 2018
 Duration (year-month-day) from 2018-08-01   to  2020-01-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    CORTI APS DK (KOBENHAVN) coordinator 1˙439˙183.00

Map

 Project objective

Out-of-Hospital Cardiac Arrest (OHCA) is one of the leading causes of death worldwide. It is a time-critical condition with survival chances decreasing by 10% with every minute of delay from collapse to defibrillation. Currently, Emergency Medical Services (EMS) dispatchers use guidelines to recognise OHCA during emergency calls prior to activating the emergency response system. EMS are struggling as emergency calls have increased in Europe from 100 million calls in 2003 to 320 million in 2016. Thus, assistant decision tools will be necessary to help EMS to faster identify OHCA situations.

Our solution, AI4EMS, is the first and only smart digital assistant for EMS dispatchers that supports the triage decision-making by: 1) processing and analysing emergency calls in real-time; 2) recognising OHCA in an evidence-based process from large amounts of historical data (unfeasible to humans); and 3) presenting the most important insights to the EMS dispatcher in a user friendly manner. AI4EMS allows for faster (reducing almost 3 minutes on average) and more accurate (increase from 73.9% human accuracy to 95%) OHCA recognition by leveraging advanced speech analytics and AI. We offer a user-friendly and secure SaaS solution capable of communicating using Natural Language, accessed via a Nvidia TX1-based device. We are directly supporting the eHealth Action Plan 2012-2020 and Digital Single Market (DSM) strategies, by providing a disruptive ICT technology to improve EMS dispatch efficiency and triage accuracy – which will impact the economy and society at large.

With the upgrade and commercialisation of AI4EMS we will disrupt the Artificial Intelligence (AI) market for healthcare taking a step further on our goal to become world leaders in EMS artificial intelligence. Forecasted sales will render revenues of €86.7 million in the first five years of commercialization and a total of 127 new jobs will be created by 2024.

 Deliverables

List of deliverables.
AI4EMS online content Websites, patent fillings, videos etc. 2019-08-01 13:01:00

Take a look to the deliverables list in detail:  detailed list of AI4EMS deliverables.

 Publications

year authors and title journal last update
List of publications.
2020 Andreas Cleve, Dimitri Devillers, Matteo Palladini, Jerome Paris, Rose Michael, Etienne Faure, Rodolfo Bonora
Detecting Out-of-Hospital Cardiac Arrest Using Artificial Intelligence
published pages: , ISSN: , DOI:
2020-02-13
2019 Valentin Liévin, Andrea Dittadi, Lars Maaløe, Ole Winther
Towards Hierarchical Discrete Variational Autoencoders
published pages: , ISSN: , DOI:
NeurIPS Workshop on Advances in Approximate Bayesian Inference 2020-02-13
2017 Marius Paraschiv, Lasse Borgholt, Tycho Max Sylvester Tax, Marco Singh, Lars Maaløe
Exploiting Nontrivial Connectivity for Automatic Speech Recognition
published pages: , ISSN: , DOI:
NIPS workshop on machine learning for audio 2019-08-05
2019 Lars Maaløe, Marco Fraccaro, Valentin Liévin, Ole Winther
BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
published pages: , ISSN: , DOI:
arXiv preprint arXiv:1902.02102 2019-08-05
2017 Tycho Max Sylvester Tax, Jose Luis Diez Antich, Hendrik Purwins, Lars Maaløe
Utilizing Domain Knowledge in End-to-End Audio Processing
published pages: , ISSN: , DOI:
31st Conference on Neural Information Processing Systems (NIPS 2017) 2019-08-05
2019 Stig Nikolaj Blomberg, Fredrik Folke, Annette Kjær Ersbøll, Helle Collatz Christensen, Christian Torp-Pedersen, Michael R. Sayre, Catherine R. Counts, Freddy K. Lippert
Machine learning as a supportive tool to recognize cardiac arrest in emergency calls
published pages: 322-329, ISSN: 0300-9572, DOI: 10.1016/j.resuscitation.2019.01.015
Resuscitation 138 2019-08-06
2018 Jan Kremer, Corti, Copenhagen, Denmark, jk@corti.ai Lasse Borgholt, Corti, Copenhagen, Denmark, lb@corti.ai Lars Maaløe , Corti, Copenhagen, Denmark, lm@corti.ai
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition
published pages: , ISSN: , DOI:
32nd Conference on Neural Information Processing Systems (NeurIPS 2018) 2019-08-05

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The information about "AI4EMS" are provided by the European Opendata Portal: CORDIS opendata.

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