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 €
0EC-Contrib. €
0Partnership
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0AI4EMS 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.
Project "AI4EMS" data sheet
The following table provides information about the project.
| Coordinator |
CORTI APS
Organization address contact info |
| 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.
| # | ||||
|---|---|---|---|---|
| 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
| 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 |
|---|---|---|---|
| 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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