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Center for Medical Data Science

Our Institutes of
Center for Medical Data Science About us

Univ.-Prof. Dr. Martin Posch

Vision/Mission

Technology transforms medicine into data science. We are working on a future in which each person is treated in full consideration of their individual characteristics, environment and needs. Our research creates knowledge from data, leading to healthier individuals and a resilient society.

The Center for Medical Data Science combines cutting-edge methodological know-how from a range of quantitative domains to foster data-driven medical research. It provides a platform for research, collaboration and education in medical data science at the Medical University of Vienna.

We strive toward advancing algorithms and quantitative methods in statistics, mathematics, computer science and related fields to develop advanced experimental design and analysis methods of data in biology and medicine, including clinical and molecular data, and applying them to concrete research questions in cooperation with biologists and clinicians at the Medical University of Vienna, as well as with national and international cooperation partners.

News All News
11. August 2026

Computer simulations improve the design of clinical trials

Computer simulations can help to improve the design of clinical trials during the planning phase. This is the conclusion reached by an international…
07. August 2026

MedUni Vienna Launches the 2ReDIF-Health Project to Strengthen Resilient Health Data Infrastructure in Rwanda and Tanzania

Led by Dr. Adnan Jouned, the 2ReDIF-Health project equips partner institutions in Tanzania and Rwanda with an open-source platform for federated…
01. July 2026

MedUni Vienna and the PMDA strengthen their collaboration in regulatory science

Memorandum of Understanding signed with Japanese Pharmaceuticals and Medical Devices Agency
01. July 2026

Moritz Schaefer is Researcher of the Month July 2026

Multimodal learning enables chat-based exploration of single-cell data

Research

Institute of Artificial Intelligence
Institute of Clinical Biometrics
Institute of Medical Statistics
Institute of Medical Information Management
Institute of Outcomes Research
Institute of the Science of Complex Systems

Study, Education and Training

Overview of Our Courses in WS 2024/25
The Medicine Degree Programme
The Medical Informatics Master’s Programme
The Molecular Precision Medicine Master’s Programme
PhD Programme “Medical Informatics, Biostatistics and Complex Systems”
>> As of 3.10.: Open Lecture “Health 4.0 – Digital Transformation in Healthcare”
Selected Publications All Publications
Barata C., Rotemberg V., Codella N.C.F., Tschandl P., Rinner C., Akay B.N., Apalla Z., Argenziano G., Halpern A., Lallas A., Longo C., Malvehy J., Puig S., Rosendahl C., Soyer H.P., Zalaudek I., Kittler H.

A Reinforcement Learning Model for AI-Based Decision Support in Skin Cancer

We investigated whether human preferences hold the potential to improve diagnostic artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case. We utilized nonuniform rewards and penalties based on expert-generated tables, balancing the benefits and harms of various diagnostic errors, which were applied using reinforcement learning.

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Selected Publications All Publications
Stamm T.A., Partheymüller J., Mosor E., Ritschl V., Kritzinger S., Alunno A., Eberl J.-M.

Determinants of COVID-19 Vaccine Fatigue

There is growing concern that Coronavirus Disease 2019 (COVID-19) vaccine fatigue will be a major obstacle in maintaining immunity in the general population. In this study, Tanja Stamm et al. assessed vaccine acceptance in future scenarios in two conjoint experiments, investigating determinants such as new vaccines, communication, costs/incentives and legal rules.

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Selected Publications All Publications
Schaefer M., Peneder P., Malzl D., Lombardo S.D., Peycheva M., Burton J., Hakobyan A., Sharma V., Krausgruber T., Sin C., Menche J., Tomazou E.M., Bock C.

Multimodal learning enables chat-based exploration of single-cell data

Single-cell sequencing provides great insights into the inner workings of cells – but making sense of the data requires advanced bioinformatics skills. Researchers at CeMM, Medical University of Vienna, and St. Anna Children’s Cancer Research Institute have now developed an artificial intelligence (AI) method and software tool that lets scientists explore such datasets through natural-language conversations – speaking English with the computer instead of having to learn a programming language.

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Selected Software All Software
GINGER

A Web Interface for Sample Size Planning for Animal Experiments

At the Institute of Clinical Biometrics, a web-based tool for the design of animal experiments was developed. In contrast to other software for experimental design, the webtool GINGER is specialised on multi-group designs and considers benefits in power by application of ANOVA and corrections for multiplicity by the methods of Tukey, Dunnett or Bonferroni. GINGER also offers tutorials and example applications. It is based on the statistical software R-shiny.

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Selected Software All Software
NCC

Design and Analysis of Platform Trials with Non-Concurrent Controls

This software, developed at the Institute of Medical Statistics, provides functions for data generation, analysis, visualisation and execution of simulation studies. The implemented analysis methods are described in:

Bofill Roig M., Krotka P., Burman C.-F., Glimm E., Gold S.M., Hees K., Jacko P., Koenig F., Magirr D., Mesenbrink P., Viele K., Posch M. (2022) On Model-Based Time Trend Adjustments in Platform Trials with Non-Concurrent Controls. BMC Medical Research Methodology 22:228.

Saville B.R., Berry D.A., Berry N.S., Viele K., Berry S.M. (2022) The Bayesian Time Machine: Accounting for temporal drift in multi-arm platform trials. Clinical Trials 19(5), 490–501.

Schmidli H., Gsteiger S., Roychoudhury S., O’Hagan A., Spiegelhalter D., Neuenschwander B. (2014) Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics 70(4), 1023–1032.

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Selected Software All Software
LOLA

Locus Overlap Analysis for Enrichment of Genomic Ranges

LOLA implements easy-to-use (but also fully scriptable) enrichment analysis for genomic region sets, thus facilitating the interpretation of functional genomics and epigenomics data. Usage statistics: ~10,000 users (unique IP downloads from Bioconductor) since 2015.


Sheffield NC, Bock C (2016). “LOLA: Enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor.” Bioinformatics. R package version 1.32.0, http://code.databio.org/LOLA.

Access the “LOLA” Programme