Research

Biostatistical methods are applied and enhanced by our team in all phases of research projects as part of scientific collaboration with clinics, centers and institutes of the Medical University of Graz.

• Research team of Andrea Berghold

The Evidence-based Medicine Review Center has a common research focus with the Institute of General Practice and Evidence-based Health Services Research. Goals include drawing up systematic reviews on specific medical questions as the basis for guidelines, patient information and support in decision-making for general practitioners.

• Research team of Klaus Jeitler

HCI4MED works on methodological approaches to human-centered artificial intelligence (HCAI) in order to improve human health. The team pursues a synergistic approach that combines human-computer interaction and machine learning with knowledge discovery in databases (HCI-KDD) with the goal of aligning AI with human, ethical and legal values. The two main topics are explainability and robustness.

• Research team of Andreas Holzinger

E-learning with digital media that can be completed at any time and from any location is becoming increasingly important in medical education. In this connection, great progress has been made in microlearning on mobile end devices. Related research focuses on user behavior and the learning effectiveness of e-learning and microlearning opportunities and serves as the basis for further development carried out in close cooperation with the "Teaching with Media" staff unit.

• Research team of Josef Smolle

Patient-reported outcomes (PRO) take into account the perspective of patients in daily medical practice as well as in research and quality management projects. Our work consists of developing PRO on the basis of modern theoretical test approaches in order to make reliable statements on topics relevant to patients (such as pain or disease-specific quality of life).

• Research team of Alexander Avian

Semantic modeling of disease and clinical processes supports the secondary use of clinical data. Its structure determines methods and tools for pursuing goals such as research and decision-making support. Our researchers focus on the three main points it has yielded:

• Standardization through ontologies, terminologies and information models
• Extraction of information from clinic texts
• Probabilistic models that can be learned by machines from data and data extracts

• Research team of Stefan Schulz