Explore the words cloud of the BigMedilytics project. It provides you a very rough idea of what is the project "BigMedilytics" about.
The following table provides information about the project.
Coordinator |
PHILIPS ELECTRONICS NEDERLAND BV
Organization address contact info |
Coordinator Country | Netherlands [NL] |
Project website | http://www.bigmedilytics.eu |
Total cost | 16˙940˙837 € |
EC max contribution | 14˙997˙306 € (89%) |
Programme |
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)) |
Code Call | H2020-ICT-2017-1 |
Funding Scheme | IA |
Starting year | 2018 |
Duration (year-month-day) | from 2018-01-01 to 2021-02-28 |
Take a look of project's partnership.
There are three main reasons for an immediate innovation action to apply big data technologies in Healthcare. Firstly, a Healthy nation is a Wealthy nation! An improvement in health leads to economic growth through long-term gains in human and physical capital, which ultimately raises productivity and per capita GDP. Secondly, Healthcare is one of the most expensive sectors, which accounts for 10% of the EU’s GDP continuously becoming more expensive. Thirdly, as healthcare is traditionally very conservative with adopting ICT, while big healthcare data is becoming available, the expected impact of applying big data technologies in Healthcare is enormous. BigMedilytics will transform Europe’s Healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously, covering the entire Healthcare Continuum – from Prevention to Diagnosis, Treatment and Home Care throughout Europe. BigMedilytics produces: • A Big Data Healthcare Analytics Blueprint (defining platforms and components), which enables data integration and innovation spanning all the key players across the Healthcare Data Value Chains • Instantiations of the Blueprint which implement BigMedilytics concepts across 12 large-scale pilots accounting for an estimated 86% of deaths and 77% of the disease burden in Europe • The Best “Big Data technology and Healthcare policy” Practices related to big data technologies, new business models and European and national healthcare data policies and regulations. BigMedilytics will maximize the impact by using its Big Data Healthcare Analytics Blueprint and the Best Practices to scale-up the concepts demonstrated in the 12 pilots, to the whole Healthcare sector in Europe. It will use health records of more than 11 million patients across 8 countries and data from other sectors such as insurance and public sector.
T0 base line measurement of the KPIs | Documents, reports | 2020-04-11 04:55:55 |
Country specific infographics that summarize the relevant regulations for Big data technologies in the healthcare sector | Documents, reports | 2020-04-11 04:55:55 |
Dissemination procedures | Documents, reports | 2020-04-11 04:55:55 |
Website portal | Documents, reports | 2020-04-11 04:55:55 |
Intermediate report on dissemination activities | Documents, reports | 2020-04-11 04:55:56 |
Communication plan and tools | Documents, reports | 2020-04-11 04:55:55 |
Initial prototypes of specific components for all BigMedilytics pilots (software) | Documents, reports | 2020-04-11 04:55:55 |
Updated prototypes of specific components for all BigMedilytics pilots (software) | Documents, reports | 2020-04-11 04:55:55 |
Take a look to the deliverables list in detail: detailed list of BigMedilytics deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2020 |
Jose Luis Holgado, Cristina Lopez, Antonio Fernandez, Inmaculada Sauri, Ruth Uso, Jose Luis Trillo, Sara Vela, Julio Nuñez, Josep Redon, Adrian Ruiz Acute kidney injury in heart failure: a population study published pages: , ISSN: 2055-5822, DOI: 10.1002/ehf2.12595 |
ESC Heart Failure | 2020-04-11 |
2019 |
Nils Rethmeier, Barbara Plank MoRTy: Unsupervised Learning of Task-specialized Word Embeddings by Autoencoding published pages: 49-54, ISSN: , DOI: 10.18653/v1/w19-4307 |
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) | 2020-04-11 |
2018 |
Veeningen, Meilof; Chatterjea, Supriyo; Horváth, Anna Zsófia; Spindler, Gerald; Boersma, Eric; Spek, Peter; Galiën, Onno; Gutteling, Job; Kraaij, Wessel; Veugen, Thijs Enabling analytics on sensitive medical data with secure multi-party computation published pages: , ISSN: , DOI: 10.3233/978-1-61499-852-5-76 |
2 | 2020-04-11 |
2018 |
Thaler, Stefan; Menkovski, Vlado; Petkovic, Milan Deep Learning in Information Security published pages: , ISSN: , DOI: |
1 | 2020-04-11 |
2019 |
Gaurav Vashisth, Jan-Niklas Voigt-Antons, Michael Mikhailov, Roland Roller Exploring Diachronic Changes of Biomedical Knowledge using Distributed Concept Representations published pages: 348-358, ISSN: , DOI: 10.18653/v1/w19-5037 |
Proceedings of the 18th BioNLP Workshop and Shared Task | 2020-04-11 |
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "BIGMEDILYTICS" project.
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Send me an email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.
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The information about "BIGMEDILYTICS" are provided by the European Opendata Portal: CORDIS opendata.
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