Being fit and healthy even at high age is a high value for many people. A key factor in this is a healthy diet. People, who eat well and healthily throughout their lives, are less likely to be ill in old age. But how exactly is healthy nutrition defined? How healthy do older people in Germany eat? Would they even accept new nutrition recommendations? The competence cluster Nutritional Intervention for Healthy Aging (NutriAct) is investigating these questions in the Berlin-Potsdam area. The aim is to create the scientific basis for age-appropriate nutritional interventions and recommendations. In addition, new foods are to be developed that enable healthy nutrition. The cluster focuses in particular on the middle-aged population (50-70 years) in order to promote healthy aging beyond the age of 70. To this end, the neurobiological, psychological, social and familial basis of food choices and, in particular, their influencing factors within family structures, will be analyzed. In addition, research will be conducted to determine whether sociocultural factors influence taste preferences and dietary practices. A central question here is the significance of gender-specific effects in connection with taste orientations within a partnership. In addition, the extent to which moments of upheaval, such as the transition to retirement, separation, or an illness, influence eating practices will be examined. On the basis of the nutrition pattern analyses, realistically implementable nutrition recommendations will be developed. The central element of the cluster is an intervention study in which the effectiveness, adherence and acceptance of the NutriAct dietary pattern rich in plant proteins, fiber and unsaturated fatty acids will be systematically tested. Based on this, new healthy and palatable foods will be developed that will be accepted by consumers. Accompanying this will be the identification of new biomarkers that predict the effects of a particular diet on health status, especially in old age.
The German-Canadian consortium NephroCAGE is cooperating to demonstrate the added value of artificial intelligence (AI) using the concrete clinical example of kidney transplantation. Inadequate kidney function requires regular dialysis: there are currently around 100,000 dialysis patients in Germany, and around half that number in Canada. Dialysis costs approximately 30-40k EUR per patient per year. In comparison, a kidney transplant costs around 15-20 thousand euros. In 2019, more than 2,100 kidney transplants were performed in Germany (German Organ Transplant Foundation) and more than 1,700 in Canada (Canadian Institute for Health in Canada). However, suitable donor organs are rare: in Germany, for example, there are more than 7,000 patients on a waiting list, and in Canada more than 3,000. Even after a transplant, there is a risk of complications that can lead to severe limitations in kidney function or, in the worst case, even total loss of the organ.
The consortium partners are creating a learning AI system to match organ donors and recipients even more precisely in advance (matching) and thus prevent risks in kidney transplants. To this end, clinical centers of excellence in both nations are contributing transplant data from the last ten years. They will be analyzed using AI learning techniques and combined together with a novel matching algorithm to create clinical prognostic models for kidney transplant patients. By using a federated learning approach, where the algorithms are executed at the location of the data, data protection is maintained and sensitive health data from both nations can serve as a common basis for clinical prognostic models for the first time. As a result, a clinical demonstrator will be created to serve the exploitation of the medical and technical innovations in the context of care, as well as a basis for exploitation and follow-on projects.
The University of British Columbia, Faculty of Medicine, Department of Pathology and Laboratory Medicine, Vancouver, British Columbia
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
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)
Borchert, F., Lohr, C., Modersohn, L., Hahn, U., Langer, T., Wenzel, G., Follmann, M., Schapranow, M.-P.: "Herr Doktor, verstehen Sie mich?“: Wie lernende Systeme helfen medizinische Fachsprache zu verstehen und welche Rolle klinische Leitlinien dabei spielen.gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland.13,19--22 (2020).
Borchert, F., Lohr, C., Modersohn, L., Langer, T., Follmann, M., Sachs, J.P., Hahn, U., Schapranow, M.-P.: GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines.Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis. bl. 38--48. Association for Computational Linguistics, Online (2020).
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).