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", Master's Program in Epidemiology, Charité
SS 2020: Module "Ethics and Policy Questions", Master’s Program in Bioinformatics, FU Berlin
WX 2019/20: Module "Artificial Intelligence in Medicine", Medicine, Charité
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.