Tag Archives: Consortium

Big Medilytics Consortium

Executive Summary

BigMedilytics is an international collaboration between partners from academia and industry across Europe. It aims to 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.

In particular, we are focusing on applying latest big data and machine learning technologies to the use case nephrology to measure and analyze clinical performance indicators, integrate predicitive models, and measure their impact on clinical routine.

Project Partners

  • Philips Electronics Nederland B.V., Netherlands
  • Fundacion Pala La Investiogation Del Hospital Clinico De La Comunitat Valencia, Fundacion Incliva, Spain
  • Instituto Technologico De Informatica, Spain
  • ERASMUS Universitait Medisch Centrum Rotterdam, Netherlands
  • ACHMEA BV, Netherlands
  • GIE AXA, France
  • OPTIMEDIS AG, Germany
  • ATOS Spain SA, Spain
  • Nederlandse Organisatie voor Toegepast-natuurwetenschappelijk onderzoek TNO, Netherlands
  • Technische Universiteit Eindhoven, Netherlands
  • HUAWEI Technologies Düsseldorf GMBH, Germany
  • Royal College of Surgeons in Ireland, Ireland
  • Stockholms Lans Landsting, Sweden
  • National Center for Scientific Research “Demokritos”, Greece
  • Athens Technology Center SA, Greece
  • Rheinische Friedrich-Wilhelms-Universität Bonn Germany
  • Universidad Politecnica de Madrid, Spain
  • Servicio Madrileño de Salud, Spain
  • Medizinische Universität Wien, Austria
  • IBM Israel – Science and Technology Ltd., Israel
  • Institut Curie, France
  • Teknologian tutkimuskeskus VTT Oy, Finland
  • Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Germany
  • Charité – Universitätsmedizin Berlin, Germany
  • AOK Nordost – Die Gesundheitskasse, Germany
  • Universitätsklinikum Essen, Germany
  • University of Southampton, United Kingdom
  • my mhealth limited, United Kingdom
  • ASTRAZENECA UK LIMITED, United Kingdom
  • Onze Lieve Vrouwe Gasthuis, Netherlands
  • Stichting Elisabeth-TweeSteden Ziekenhuis, Netherlands
  • ERASMUS Universiteit Rotterdam, Netherlands
  • Privredno Drustvo za Pruzanje Usluga Istrazivanje | Razvoj Nissatech Innovation Centro Doo, Serbia
  • Hasso Plattner Institute and AnalyzeGenomes.com

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    HiGHmed Medical Informatics Consortium

    Executive Summary

    The HiGHmed consortium aims to develop and use innovative information infrastructures to increase the efficiency of clinical research and to swiftly translate research results into validated improvements of patient care. These aims are tightly connected with challenges to integrate and further develop solutions of innovative, internationally interoperable data integration and methods, with the aim to demonstrate their added value for health research and patient care. The concepts must be designed in a way that will help to develop sustainable structures and with the perspective for an easy roll-out to other hospitals. You might want to refer to the HiGHmed consoritum website for further details.

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    Systems Medicine of Heart Failure (SMART)

    Executive Summary

    e:Med Flyer 2016The cooperation project Systems Medicine of Heart Failure (SMART) focuses on researching risk factors of heart failures. The onset and course of heart failure (HF) is triggered by a complex regulatory network that includes stressors (pressure overload by individual anatomic hemodynamic settings), intrinsic (genes), environmental (regulating epigenetics), and modifying factors (such as hormones and the immune system). SMART aims to establish individualized strategies for the prevention and management of HF by early detection of the physiological, genomic, proteomic and hemodynamic mechanisms that lead from one common cause of ventricular dysfunction (pressure overload) to maladaptive remodeling and irreversible HF. To cope with the complexity of HF, SMART will interrelate models describing the interplay between genome, proteome and cell function, regulating hormones, tissue composition and hemodynamic whole organ function up to a whole body description of a patient and patient cohorts. The ultimate goal is to demonstrate proof-of-concept tools for predicting disease evolution and efficacy of treatment in a given patient. To achieve this task SMART will apply
    – A modelling framework that couples multi-scale mechanistic models with in-depth genome/proteome, cell physiology and whole organ (biomechanical and fluid dynamic) models
    – Subsequently, investigate methods validity and relevance for “quantitative prediction” of treatment outcome in a clinical proof-of-concept trial (demonstrator) of patients with aortic valve diseases.

     

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    Related Content

    In the following, we assembled related content that might be of your particular interest.

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    Publications

    Research Publications

    • Kraus, M., Schapranow, M.-P.: An In-Memory Database Platform for Systems Medicine. Proceedings of the International Conference on Bioinformatics and Computational Biology. bl. 93--100. The International Society for Computers and Their Applications (ISCA) (2017).
    • Schapranow, M.-P., Kraus, M., Danner, M., Plattner, H.: IMDBfs: Bridging the Gap between In-Memory Database Technology and File-Based Tools for Life Sciences. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. bl. 1133--1139. IEEE (2016).

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