Research topics at the Center for Medical Data Science

The six sections of the Center for Medical Data Science are dedicated to quantitative and methodological research in medicine and life sciences.

Research at the Section for

• Artificial Intelligence is focused on the development of intelligent medical application systems using artificial intelligence and computer-based decision support methods and to improve and expand the range of methods of artificial intelligence. The focus is on machine learning, decision support, natural language processing, and knowledge-based systems.
   
  
• Clinical Biometrics covers methodological research in the fields of statistical modeling, prediction, causal inference, survival analysis, and analysis of high-dimensional (omics) data. The section applies biostatistical methods in clinical cooperation and develops innovative methodology for biostatistical applications.
   
• Medical Statistics develops statistical methodology for innovative clinical trial designs including adaptive designs, multiple hypothesis testing and regulatory statistics. Other main research topics are high dimensional data analysis, model selection and epidemiological studies. The section engages in the design and analysis of clinical trials in numerous collaborative projects with clinical researchers.
   
• Medical Information Management covers the documentation, communication, and retrieval of medical information, visual data analysis and medical imaging with a focus on patient-centered, cross-organizational health information. Current research topics include the standardization of electronic health records, information retrieval, reuse of health information, and visual data analysis and image processing in medicine.
   
  
• Outcomes Research conducts methodological research in the areas of patient-reported outcomes, complex indices, mixed methods, activity and motion analyses, as well as outcomes in health services research, patient-centered care and assistive technologies. The collection of data is optimized with sensor technologies and e-Health solutions. Furthermore, qualitative research methods are developed and applied.
   
• Science of Complex Systems deals with quantitative complex systems research. The research topics include complex living matter (artificial cell, genomics, biological time-series), complex social systems (statistical measures of societies, financial markets, efficiency and bureaucracy), and physics of social systems (statistical mechanics, network theory, systemic risk, physics of evolution).

For detailed information on the research topics please visit the websites of the respective sections via the links provided above.

Furthermore, two research priorities exist across our institutions:

Data Science for Personalized Medicine

Personalized Medicine is mainly aimed at translating a patient’s genetic or other biomolecular information into individualized diagnostic or therapeutic decisions that are specifically tuned to or optimized toward the patient’s organism.

Data Science brings together fields like statistics, mathematics, or computer science for advanced data analysis and modeling in order to support the goals of personalized medicine.

For further information please visit the website cemsiis.meduniwien.ac.at/ds4pm/.
 

Re-USE of health data for medical research

“Data are too expensive to waste”
(Frank Harrell Jr., 2001)

By “Re-USE” we mean the utilization of data for purposes other than those originally intended. We are restricting this definition to human data obtained in a clinical or health care context, and their utilization for purposes of medical research.

For further information please visit the website cemsiis.meduniwien.ac.at/re-use/.