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

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

A large proportion of relevant information is contained in clinical free text with varying degrees of structure and standardization. Data-driven language technologies can support the structuring and standardization of such information based on international standards. The research group focuses on the adaptation and evaluation of current technologies for generating structured patient profiles as well as FAIR-compliant data representations for secondary use scenarios.

• Research team of Markus Kreuzthaler

Our research concentrates on the field of the automated and computer-assisted evaluation of medical image data, with a focus on radiological images, the aim of which is to support experts through artificial intelligence (AI) and machine learning. Our interests lie in both the methodological principles and medical applications, in order to be able to apply image analysis for the long term improvement of patient care.

• Research team of Martin Urschler