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.
Click to download executive summary (German only).
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.
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)
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.
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).