Medical Informatics

A list of our publications can be found here. 

Responsible
SS 2023: Module "Introduction to Medical Informatics", Master's Program in Epidemiology, Charité - More information
SS 2023: Module "Modern Methods of Medical Informatics", Master's Program in Epidemiology, Charité
SS 2022: Module "Introduction to Medical Informatics", Master's Program in Epidemiology, Charité - More information
WS 2021/22: Seminar "Current Topics in Medical Informatics" - More information
SS 2021: Seminar "Current Topics in Medical Informatics" - More information
SS 2021: Module "Introduction to Medical Informatics", Master's Program in Epidemiology, Charité
WS 2020/21: Seminar "Current Topics in Medical Informatics" - More information
SS 2020: Module "Introduction to Medical Informatics", Master's Program in Epidemiology, Charité

Contributing
WS 2022/23: Module "Artificial Intelligence in Medicine", Medicine, Charité
WS 2022/23: Lecture MET3, PhD Program in Health Data Sciences, Charité
WS 2021/22: Module "Artificial Intelligence in Medicine", Medicine, Charité
WS 2021/22: Lecture MET3, PhD Program in Health Data Sciences, Charité
WS 2020/21: Lecture MET3, PhD Program in Health Data Sciences, Charité
WS 2020/21: Curriculum, BIH Digital Clinician Scientist Program
WS 2020/21: Module "Artificial Intelligence in Medicine", Medicine, Charité 
SS 2020: Module "Ethics and Policy Questions", Master's Program in Bioinformatics, FU Berlin
WS 2019/20: Module "Artificial Intelligence in Medicine", Medicine, Charité

Further events
2021: Seminar "Sharing Sensitive Data in a Reusable and Legal Way" - More information
2020: Seminar "Sharing Sensitive Data in a Reusable and Legal Way" - More information

You can find our open source software on our GitHub page. We further develop and operate services for the integration, analysis, and sharing of health data at BIH and Charité. Key examples include:

• Clinical Data Warehouse platform with self-service analytics capabilities, based on Informatics for Integrating Biology and the Bedside (i2b2), for example for BeLOVE.
• Real-World-Evidence platform, built upon the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and tools from the Observational Health Data Science and Informatics (OHDSI) program, connected to the European Health Data Evidence Network (EHDEN).
• Data integration solutions connected to the national platform of the Medical Informatics Initiative (MII) in collaboration with the Medical Data Integration Center (MeDIC) associated with us.
• Platform for capturing study data and Patient-Reported Outcome Measures based on the OpenEDC software of the University Hospitals of Münster and Heidelberg.
• Consulting and technical solutions related to the anonymization and pseudonymization of medical data.
• Information infrastructures and research data management solutions, for example, for the SFB 1340 in partnership with the Clinic for Radiology and the Institute for Medical Informatics.

HiGHmed: The partner sites of the HiGHmed consortium in the German Medical Informatics Initiative (MII) establish Medical Data Integration Centers (MeDICs), which integrate and harmonize data from healthcare and research systems. Important tasks include ensuring data quality and data protection to enable data sharing using the MII infrastructure. The added value of the solutions established will be demonstrated in various clinical and research use cases. 

Further information

PrivateAIM: The goal of the PrivateAIM project is to develop a federated platform for privacy-preserving machine learning and data analytics in the MII. The general concept is that the analyses will come to the data, not the data to the analyses.

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RISK PRINCIPE: The objective of this project is the development and implementation of a data-driven, risk-stratified infection control process using an automated monitoring system to effectively and efficiently minimize hospital bacteremias.

Open Medical Inference: The project's aim is to simplify the use of AI in medicine by establishing a network of providers offering their AI models as "turnkey" services.

GeMTeX: The project's goal is to create a large annotated corpus of German medical texts from patient care, making them accessible in anonymized form for research.

NUM-RDP: The NUM-RDP project operates a central platform for the aggregation and use of routine healthcare data in the University Medicine Network. It forms the technical basis for existing and future research projects that want to use the platform or and/or extend it with new software components. It receives, stores and provides data in an interoperable, open format. The Medical Informatics Group supports the project in the area of technical data protection and information security. 

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NUM-DIZ: In the "NUM-DIZ" project, the Data Integration Centers established at the university hospitals as part of the Medical Informatics Initiative are operated as an infrastructure within the Network of University Medicine.

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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. 

Further information

The European Health Data & Evidence Network (EHDEN) aims to answer research questions based on large data sets distributed across Europe while preserving the anonymity of patients. With support from EHDEN, the Charité is building an infrastructure based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and the associated tools of the Observational Health Data Sciences and Informatics (OHDSI) program to make health data accessible for research in a structured form. 

Further information

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. 

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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.

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In the project "AI-based Anonymization in Medicine" (KI-AIM), an anonymization platform is being developed to provide large amounts of realistic health data. Ideally, these data are not persomal and can therefore be used more easily under data protection law. This could facilitate access to data for research and the commercial development of data-based medical solutions. 

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The establishment of the German Research Data Centre (Forschungsdatenzentrum; FDZ) at the Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte; BfArM) promises to significantly improve the secure use of health data for research purposes in Germany. As part of this process the BIH Medical Informatics Group participates in a project investigating innovative data anonymization and synthetization techniques, with the aim of ensuring the best possible protection of patient privacy. 

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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. 

Further information

The Collaborative Research Center (CRC) 1340 "Matrix in Vision" investigates innovative methods for in vivo visualization of the pathologically altered extracellular matrix. Our group supports the CRC with research data management, analysis and visualization through an information infrastructure project. Here, the focus is on the entire lifecycle of different types of complex and multidimensional data. 

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The aim of the THS-MED project is to develop highly scalable open source tools for trust centers in translational medicine. A key focus is on components for patient data management and pseudonymization, both for study personnel and researchers, as well as for programmatic access.

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CORD_MI: 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. 

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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. 

Further information

In CODEX+ the collaborative research infrastructure CODEX of the Network University Medicine (NUM) will be rolled out more broadly and developed further, with a focus on new data sources, common data models, data anonymisation and convergence with the structures of the Medical Informatics Initiative and further NUM projects. 

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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. 

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ORCHESTRA is a large-scale research project aimed at combating the coronavirus pandemic. The aim is to establish an international cohort for conducting retrospective and prospective studies in order to generate rigorous evidence to improve the prevention and treatment of COVID-19 and to be better prepared for future pandemics.

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