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.
Project Partners
Canada
The University of British Columbia, Faculty of Medicine, Department of Pathology and Laboratory Medicine, Vancouver, British Columbia
The project is generously sponsored by the German Federal Ministry for Economic Affairs and Climate Action (2021-2022) as project of strategic interest.
Today, we are happy to announce the general availability of the 2019-nCoV Coronavirus Data Analysis Tool by HPI. After days of closed beta phase, we just finished integrating the valuable user feedback. Finally, the 2019-nCoV Coronavirus Data Analysis Tool is available for public use. Furthermore, we decided that improvements and functional extensions from now on will be directly integrated in the productive version. Thus, users can benefit much faster from them. Currently, we are working on adding additional situation reports to provide valuable insights on the latest development of the pandemia.
PS.: If you think that the tool is helpful, feel free to share the link. If you miss specific data or functionality, please feel free to send us your feedback to extend the functionality.
Today, treatment alternatives for every oncology patient are individually discussed by medical
experts in so-called tumor boards. However, the molecular analysis of each tumors is very complex and the assessment of individual tumor variants time-consuming. Therefore, specialized molecular tumor boards focus also on the analysis and therapy assessment incorporating fine-grained molecular data.
We worked together with subject-matter experts to defined and evaluate a standardized clinical process, designed adequate tool support, and integrated relevant data source for an improved tumor board experience in molecular tumor boards. For example, we applied the scrum board approach, which is well-known from software engineering, to the clinical challenge.
Background
Molecular analysis of tumors is the key driver of precision medicine in oncology. It enables the matching of mutations to targeted drugs and hence more personalized treatments. With its steady advances and ever decreasing costs, it is likely to become a standard procedure for diagnosis and treatment. In molecular tumor boards, regular meetings among medical experts, treatment options are discussed and suggested for each patient based on the tumor’s genetic markup. A molecular tumor board (MTB) consists of 4 major phases: preparation, meeting, treatment suggestion documentation and follow-up. Our MTB Support Engine aims to streamline the entire process in order to tackle the key challenges of molecular tumor boards. The tool lends the Scrum methodology that has already proven itself in software development.
The most critical part of a MTB is the preparation. Genetic variants need to be researched upon available medication. For this purpose, oncologists query various databases and search engines. This is time-consuming and therefore constitutes a key bottleneck for a comprehensive rollout. The MTB support engine accelerates the preparatory research work by
Showing available information resources for a given variant
Ranking and visualizing variants by various criteria and displaying most important ones first.
Simplifying the saving of reviewed information in a click-and-collect fashion
The MTB Support Engine furthermore lets doctors generate a presentation view of relevant information with a single click. In addition, an emphasis it put on collaborative work. Every participant has access to the system allowing preparing themselves. During the meeting, decided upon treatment suggestions can be documented for inspection by the treating physician and allows a structured follow-up.
Most importantly, all information including researched annotations are saved. This allows the rapid retrieval of historic cases with genetically similar patients. Leveraging data integrated from multiple hospitals, even the discovery of novel correlations between variants and diseases is made possible in the long run.
1.Borchert, F., Lohr, C., Modersohn, L., Witt, J., Langer, T., Follmann, M., Gietzelt, M., Arnrich, B., Hahn, U., Schapranow, M.-P.: GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers. Proceedings of the Language Resources and Evaluation Conference. bll. 3650–3660. European Language Resources Association, Marseille, France (2022).
2.Henkenjohann, R., Bergner, B., Borchert, F., Bougatf, N., Hund, H., Eils, R., Schapranow, M.-P.: An Engineering Approach towards Multi-Site Virtual Molecular Tumor Board Software Support. In: Pissaloux, E., Papadopoulos, G., Achilleos, A., en Velázquez, R. (reds.) ICT for Health, Accessibility and Wellbeing. IHAW 2021. bll. 156–170. Springer, Cham (2022).
3.Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021).
4.Rasheed, A., Borchert, F., Kohlmeyer, L., Henkenjohann, R., Schapranow, M.-P.: A Comparison of Concept Embeddings for German Clinical Corpora. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). bll. 2314–2321 (2021).
5.Borchert, F., Meister, L., Langer, T., Follmann, M., Arnrich, B., Schapranow, M.-P.: Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA Annual Symposium Proceedings. bll. 237–246. American Medical Informatics Association (2021).
6.Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. 22, (2021).
7.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).
8.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. bll. 38–48. Association for Computational Linguistics, Online (2020).
Click to download executive summary (German only).
Executive Summary
The portion of elderly people within the Germany population is steadily increasing. As a result, the demand for adequate elderly care services is high. Currently, capacity planning and strategic decision taking is conducted by districts individually. As of today, this federated approach is lacking standardized analysis methods to create a holistic national view on the care topic.
The Electronic Registry for Elderly Care Services in Germany (ERPEL) (German: Elektronisches Register für Pflege-Dienstleistungen in Deutschland (ERPEL)) forms a longitudinal database of care-specific measures from individual districts to form a holistic national overview. For example, it contains details about available elderly care services, the current available capacity, demand for a specific service in a specific region.
ERPEL allows the up-to-date quantification of offer and demand for elderly care services across geographical regions. Thus, it enables a standardized methodological approach for interactive data analysis and exploration to support demand planning.
User groups of ERPEL: Family members, social worker and social planners, as well as care service providers (excerpt).
Amongst others, we are addressing the requirements of the following user groups:
Family members, who struggle to find appropriate elderly care for their relatives,
Social workers, who want to offer guidance for elderly care services,
Social planners, who aim to support governmental decision-making through provision of latest data, and
Care service providers, who are interested in the current and future demand for strategic planning and investments.
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.
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
In the following, we assembled related content that might be of your particular interest.
Events
Publications
Research Publications
1.Freitas da Cruz, H., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., Schapranow, M.-P.: 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).
2.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).
3.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).
Sponsors
2018-2021 European Commission in context of the H2020 research program.
Contact
Please feel free to get directly in touch with the experts. Use the following contact form, if you have any open questions you might to share with us.
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.
Project Partners
Heidelberg University Hospital (UKL-HD)
University Medical Center Göttingen (UMG)
Hannover Medical School (MHH)
University Hospital Schleswig-Holstein / Kiel University (UKSH)
1.Borchert, F., Lohr, C., Modersohn, L., Witt, J., Langer, T., Follmann, M., Gietzelt, M., Arnrich, B., Hahn, U., Schapranow, M.-P.: GGPONC 2.0 - The German Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline NER Taggers. Proceedings of the Language Resources and Evaluation Conference. bll. 3650–3660. European Language Resources Association, Marseille, France (2022).
2.Henkenjohann, R., Bergner, B., Borchert, F., Bougatf, N., Hund, H., Eils, R., Schapranow, M.-P.: An Engineering Approach towards Multi-Site Virtual Molecular Tumor Board Software Support. In: Pissaloux, E., Papadopoulos, G., Achilleos, A., en Velázquez, R. (reds.) ICT for Health, Accessibility and Wellbeing. IHAW 2021. bll. 156–170. Springer, Cham (2022).
3.Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021).
4.Rasheed, A., Borchert, F., Kohlmeyer, L., Henkenjohann, R., Schapranow, M.-P.: A Comparison of Concept Embeddings for German Clinical Corpora. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). bll. 2314–2321 (2021).
5.Borchert, F., Meister, L., Langer, T., Follmann, M., Arnrich, B., Schapranow, M.-P.: Controversial Trials First: Identifying Disagreement Between Clinical Guidelines and New Evidence. AMIA Annual Symposium Proceedings. bll. 237–246. American Medical Informatics Association (2021).
6.Borchert, F., Mock, A., Tomczak, A., Hügel, J., Alkarkoukly, S., Knurr, A., Volckmar, A.-L., Stenzinger, A., Schirmacher, P., Debus, J., Jäger, D., Longerich, T., Fröhling, S., Eils, R., Bougatf, N., Sax, U., Schapranow, M.-P.: Knowledge bases and software support for variant interpretation in precision oncology. Briefings in Bioinformatics. 22, (2021).
7.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).
8.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. bll. 38–48. Association for Computational Linguistics, Online (2020).
Back to school to understand the “Code of Life”. We are happy to invite you to “Code of Life” Massive Open Online Course (MOOC) hosted by openHPI. The course starts on Nov 14, 2016 and is designed as an interactive set of daily lectures followed by tasks. Sign-up for free to attend this unique course and to get your personal exam certificate at the end of the course.
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.
1.Ganzinger, M., Schapranow, M.-P.: FAIRe Datennutzung: Erfahrungen aus Verbundprojekten. gesundhyte.de: Das Magazin für Digitale Gesundheit in Deutschland. 14, 57–61 (2021).
2.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).
3.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).
4.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).
5.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).
6.Schapranow, M.-P.: Die digitale Transformation mitgestalten — Der Datenspendeausweis: Souveräner Umgang mit persönlichen Gesundheitsdaten. Plattform Life Sciences. 38–39 (2017).
7.Schapranow, M.-P.: Datenspendeausweis für Bürger: Ein Plädoyer für mündige Patienten, die die eigenen Gesundheitsdaten am besten verstehen. Management & Krankenhaus. (2016).
8.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).
9.Postel, M.: Geographical Exploration of Key Performance Indicators for Elderly Care Planning, (2016).
10.Rückert, L.: Real-time Exploration of Healthcare Data using In-Memory Database Technology, (2016).
