PRASSER LAB – MEDICAL INFORMATICS
Further advancing digital medicine requires innovative solutions for integrating healthcare and medical research data. From the perspective of medical informatics, there are important open questions. How can the required collection, integration and analysis of large and heterogeneous data sets be achieved in an efficient, effective and scalable manner? How can the trust of citizens and patients be maintained in the context of rapid change? These and related questions define the research profile of the group.
We focus on four key areas: (1) technologies for interconnecting health data and for patient participation, (2) information integration and big data architectures in translational medicine, (3) cross-site data analysis and data sharing, (4) information security and data protection in digital medicine.
Our group conducts application-oriented research to create added value for health care and medical research, as well as basic research to develop novel methods with which the underlying challenges can be tackled more effectively.
Current Research Areas
- Pay-as-you-go integration systems for health data
- Scalable information infrastructures for medical research projects
- Clinical and translational data warehousing
- Data harmonization and semantic integration
- Methods and tools for applying privacy-enhancing technologies, such as anonymization and pseudonymization, in medicine
- Secure distributed computing in healthcare
SS 2020: Module "Introduction to Medical Informatics", Masters's Program in Epidemiology, Charité
WS 2020/21: Curriculum, BIH Digital Clinician Scientist Program
SS 2020: Module "Ethics and Policy Questions", Master's Program in Bioinformatics, FU Berlin
WS 2019/20: Module "Artificial Intelligence in Medicine", Medicine, Charité
2020: Certificate "Reproducible Research" - More information
2020: Seminar "Sharing Sensitive Data in a Reusable and Legal Way" - More information
The project "National Research Data Platform" (CODEX) of the Research Network of University Medicine on COVID-19 (NUM) aims to establish a secure and interoperable national data platform for research on SARS-CoV-2 and COVID-19. This will foster research on the current and future pandemics, as scientists from a wide range of disciplines will be able to quickly access a large number of standardized datasets.
The Medical Informatics Initiative’s (MII) “Collaboration on Rare Diseases” (CORD_MI) aims to improve healthcare and research in the area of rare diseases. To achieve these goals, the technical as well as organizational solutions of the four consortia of the MII (DIFUTURE, HiGHmed, MIRACUM, SMITH) are utilized to share data on a nationwide scale.
The H2O project brings together more than 23 partners with the aim to improve the quality of care for patients by creating health outcomes observatories that will collect standardised health data in an ethical and a socially responsible way. National observatories will be federated to create a pan-European network for health outcomes data with the purpose of engaging patients and connecting providers, ultimately equipping different stakeholders with the necessary data to improve patient care.
The European Joint Programme on Rare Diseases (EJP RD) brings together over 130 institutions from 35 countries to create a comprehensive, sustainable ecosystem for research, care and medical innovation in the area of rare diseases. In the project, the BIH Medical Informatics Group works on tools and methods supporting sustainable data sharing with a specific focus on privacy aspects.
The National Research Data Infrastructure (NFDI) will systematically catalog, sustainably maintain and make databases from science and research accessible and interconnect them (inter)nationally. In this context, the goals of NFDI4Health include (1) enabling the findability of and the accessibility to structured health data, (2) establishing and improving the interoperability and reusability of health data and (3) enabling the sharing and linkage of health data while complying with data protection requirements.
The Lean European Open Survey on SARS-CoV-2 (LEOSS) is a European non-interventional prospective cohort study on the epidemiology and clinical course of SARS-COV-2 infections. The objective is to further develop evidence-based diagnostic and therapeutic recommendations. Our group contributes to this project by developing and implementing multiple layers of safeguards to support privacy-preserving data sharing.
ARX is a comprehensive open source software for anonymizing health data. It supports a wide variety of risk models, data transformation techniques and methods for analyzing the usefulness of output data. The software has been used in a variety of contexts, including commercial big data analytics platforms, research projects, clinical trial data sharing and for training purposes. ARX is able to handle large datasets on commodity hardware and it features an intuitive cross-platform graphical user interface.
At TU Munich: Technical coordinator of the consortium Data Integration for Future Medicine (DIFUTURE), which is funded under the "Medical Informatics" funding scheme of the German Federal Ministry of Education and Research (BMBF). DIFUTURE aims at data harmonization and integration, as well as secure data sharing.
At TU Munich: Co-PI of the subproject „Secure integrated big data analytics“ building a central platform for the secure collection and processing of clinical, para-clinical and experimental data for the Collaborative Research Centre (CRC) 1371.
Prasser F, Spengler H, Bild R, Eicher J, Kuhn KA. Privacy-Enhancing ETL-Processes for Biomedical Data. Int J Med Inform. 2019 06;126:72-81.
Prasser F, Kohlbacher O, Mansmann U, Bauer B, Kuhn KA. Data Integration for Future Medicine (DIFUTURE). Methods Inf Med. 2018 07;57(S 01):e57-e65.
Prasser F, Kohlmayer F, Spengler H, Kuhn KA. A Scalable and Pragmatic Method for the Safe Sharing of High-Quality Health Data. IEEE J Biomed Health Inform. 2018 03;22(2):611-22.
Prasser F, Gaupp J, Wan Z, Xia W, Vorobeychik Y, Kantarcioglu M, Kuhn K, Malin B. An Open Source Tool for Game Theoretic Health Data De-Identification. AMIA Annu Symp Proc. 2017;2017:1430-9.