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).
The 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.
Kraus, M., Mathew Stephen, M., Schapranow, M.-P.: Eatomics: Shiny exploration of quantitative proteomics data.Journal of Proteome Research. (2020).
Slosarek, T., Kraus, M., Schapranow, M.-P., Bottinger, E.: Qualitative Comparison of Selected Indel Detection Methods for RNA-Seq Data.International Work-Conference on Bioinformatics and Biomedical Engineering. bll. 166-177. Springer (2019).
Kraus, M., Hesse, G., Slosarek, T., Danner, M., Kesar, A., Bhushan, A., Schapranow, M.-P.: DEAME-Differential Expression Analysis Made Easy.Heterogeneous Data Management, Polystores, and Analytics for Healthcare. bl. 162--174. Springer (2018).
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).
Kraus, M., Niedermeier, J., Jankrift, M., Tietboehl, S., Stachewicz, T., Folkerts, H., Uflacker, M., Neves, M.: Olelo: a web application for intuitive exploration of biomedical literature.Nucleic acids research. (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).