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 BigMedilytics project use case nephrology to measure and analyze clinical performance indicators, integrate predicitive models, and measure their impact on clinical routine.
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
In the following, we assembled related content that might be of your particular interest.
Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.: MORPHER – A Platform to Support Modeling of Outcome and Risk Prediction in Health Research. Proceedings of the 19th IEEE International Conference on Bioinformatics and Biomedicine. , Athens, Greece (2019).
Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Knowledge Distillation from Machine Learning Models for Prediction of Hemodialysis Outcomes. International Journal On Advances in Life Sciences. 11, 33-43 (2019).
Freitas da Cruz, H., Pfahringer, B., Schneider, F., Meyer, A., Schapranow, M.-P.: External Validation of a “Black-Box” Clinical Predictive Model in Nephrology: Can Interpretability Methods Help Illuminate Performance Differences? Proceedings of 17th Conference on Artificial Intelligence in Medicine. bll. 191-201 (2019).
2018-2021 European Commission in context of the H2020 research program.
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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.
Heidelberg University Hospital (UKL-HD)
University Medical Center Göttingen (UMG)
Hannover Medical School (MHH)
University Hospital Schleswig-Holstein / Kiel University (UKSH)
We are proud that our Analyze Genomes platform was selected as a distinguished big data project in Germany. Read more about it in the publication “Germany — Excellence in Big Data” published by the Federal Association for Information Technology, Telecommunications and New Media (BITKOM).
The slide deck of the presentation “Real-time Exploration of the Cancer Genome, the Human Immune System (REHIS) and other NCT projects” of the workshop “Big Medical Data in Precision Medicine: Challenges or Opportunities?” on Jan 19, 2016 in London is online available now.
The aim of the Smart Analysis Health Research Access (SAHRA) cooperation project is to provide scientific analysis methods incorporating latest in-memory database technology for analysis of longitudinal health data. These methods support research and the development of innovative solutions and products by providing real-time analysis of longitudinal health data for the first time. Public and governmental institutes as well as small and mid-sized healthcare enterprises are the target audience of the project. Latest data protection and privacy measures are taken to protect any data on the SAHRA platform in compliance with latest German and International data protection laws.
Freitas da Cruz, H., Schneider, F., Schapranow, M.-P.: Prediction of Acute Kidney Injury in Cardiac Surgery Patients: Interpretation using Local Interpretable Model-agnostic Explanations. Proceedings of the 12th International Conference on Biomedical Engineering Systems and Technologies. bll. 380-387. , Prague, Czech Republic (2019).
Konak, O., Freitas Da Cruz, H., Thiele, M., Golla, D., Schapranow, M.-P.: An Information and Communication Platform Supporting Analytics for Elderly Care. 5th International Conference on Information for Ageing Well, Communication Technologies e Health (2019).
Freitas da Cruz, H., Horschig, S., Nusshag, C., Schapranow, M.-P.: Prediction of Patient Outcomes after Renal Replacement Therapy in Intensive Care. Proceedings of the 3rd International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing (2018).
Freitas da Cruz, H., Gebhardt, M., Becher, F., Schapranow, M.-P.: Interactive Data Exploration Supporting Elderly Care Planning. Proceedings of the 10th International Conference on eHealth, Telemedicine, and Social Medicine (2018).
Schapranow, M.-P.: Die digitale Transformation mitgestalten — Der Datenspendeausweis: Souveräner Umgang mit persönlichen Gesundheitsdaten. Plattform Life Sciences. 38--39 (2017).
Schapranow, M.-P.: Datenspendeausweis für Bürger: Ein Plädoyer für mündige Patienten, die die eigenen Gesundheitsdaten am besten verstehen. Management & Krankenhaus. (2016).
Schapranow, M.-P., Uflacker, M., Sariyar, M., Semler, S., Fichte, J., Schielke, D., Ekinci, K., Zahn, T.: Towards An Integrated Health Research Process: A Cloud-based Approach. Proceedings of The IEEE International Conference on Big Data. 2813--2818 (2016).
Postel, M.: Geographical Exploration of Key Performance Indicators for Elderly Care Planning, (2016).
Rückert, L.: Real-time Exploration of Healthcare Data using In-Memory Database Technology, (2016).
Horschig, F.: Prediction of Health Research Data using In-Memory Database Technology, (2016).
Durch die Auswertung großer Datenmengen ist die so genannte “Precision Medicine” entstanden. Hierbei fließen alle über einen Patienten bekannten Informationen bei der Wahl der Behandlungsentscheidung mit ein.
We are happy to contribute to the Symposium “Big Data in Medicine” taking place from July 1-2, 2015 at the Hasso Plattner Institute in Potsdam, Germany. The international symposium is an interdisciplinary cooperation between the German National Academy of Sciences Leopoldina and the Hasso Plattner Institute offering a unique opportunity for science and industry experts to work on concrete issues of personalized medicine.