The Research Centre for Computational Health addresses problems in medicine and biology using data-driven and mechanistic modeling. Important tools are machine learning for image and signal analysis, graphical networks, parameter estimation for differential equation systems and physiological simulation.

The research group specializes in modeling biological and medical systems. New approaches are developed to simulate physiological processes and to predict pathological changes. In particular, in-depth knowledge of biological/physiological processes is incorporated into multi-physics simulations.
The group develops algorithms for parameter and uncertainty estimation of physically motivated stochastic models. In particular, machine learning methods are combined with Bayesian modeling for dimensionality reduction. These methods are widely used in medicine and life sciences.

Group leader: Prof. Dr. Sven Hirsch | Learn more about the research group Biomedical Simulation

The research group applies machine learning techniques to interpret medical image data. This way, features are extracted for the characterization of disease patterns and for use as diagnostic markers. Of particular interest are the radiomic and morphological analysis of diagnostic medical imaging data. The group pursues the goal of establishing reproducible, image-based biomarkers by means of explainable artificial intelligence and ensuring their clinical utility.

The research group uses statistical and machine learning methods to model and uncover causal relations in medical data, especially to study pathophysiological processes. Categorial patient data and imaging date, e.g. magnetic resonance imaging, are processed to extract clinical knowledge.

The research group studies data from wearables and biosensors using time series analysis and combines them with biological-physical models to robustly characterize physiological systems. These data sources are used for Patient Reported Outcomes in clinical practice and for the further development of patient-centered medicine.

Group leader: Dr. Samuel Wehrli | Learn more about the research group Biosignal Analysis & Digital Health

Our GitHub organization page is the central location for the code and projects developed by the Research Centre for Computational Health. We are committed to advancing computational health through innovative research and by promoting open science [1]. Our repositories cover various topics, including algorithm development, data analysis, and computational modeling. By openly sharing our work, we aim to encourage collaboration and contribute to the wider scientific community. Embracing open science ensures that our research is transparent, reproducible, and accessible, which promotes wider dissemination and accelerates scientific progress. We welcome you to explore our projects, access our source code, and connect with us on GitHub.

Link to the GitHub