Research Centre of Computational Health
The devision of Computational Health is specialised in the confluence of data-driven and mechanistic modelling approaches in medicine and biology. The methods encompass machine learning for image and signal analysis, graphical networks, optimization of stochastic models and physiological simulations.
The centre Computational Health addresses fundamental questions in biology and medicine using computer-assisted, data-driven methods. Important tools are machine learning for image and signal analysis, parameter estimation for differential equation systems and multiphysics simulation. Effective validation strategies are used to deal with the unknown.
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.
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.
Machine learning for NMR spectroscopy
The goal of this project is to make NMR spectroscopy available to a wider range of applications and to non-experts by the automation of data reduction and analysis steps, in particular by combining deep learning methods for the extraction and a Bayesian approach for the integration and refinement of information. ...
Data-driven decision support for intracranial aneurysms and hospital catering using Bayesian networks
Clinical decisions in medicine and management decisions in facility management are regularly made on the basis of little evidence or extrapolations and are also influenced by subjective and economic aspects. While data is generated exponentially in medicine due to increasing digitization, there is no framework for ...
Data mining in neurological medicine
Restless legs syndrome (RLS, Willis-Ekbom disease) is a neurological movement disorder characterised by motor and sensory symptoms, such as the uncontrollable need to move the legs (and sometimes also the arms). Such need is associated with an unpleasant and disturbing sensation in the lower limbs that typically ...
Digital simulation for the tailored production of 3D nanofiber filters and their integration in a full protective suit for pandemic cases
We intend the tailored fabrication of 3D nanofiber aerogel (NFA) particle filters and their integration in a full protective suite with blower device for the health market. Digital simulation of the particle nanofiber interaction is used to predict the best microscopic filter geometry.
Researchers from the School of Life Sciences and Facility Management (LSFM) initiated a platform to promote interdisciplinary research in the field of health. The School of LSFM supports this initiative to increase visibility of all health-research related activities in teaching, R&D, continued education, and ...
Mack, Ines; Juchler, Norman; Rey, Sofia; Hirsch, Sven; Hoelz, Bianca; Eckstein, Jens; Bielicki, Julia,
12(8), pp. 634.
Available from: https://doi.org/10.3390/bios12080634
Castelnovo, Anna; Amacker, Julian; Maiolo, Massimo; Amato, Ninfa; Pereno, Matteo; Riccardi, Silvia; Danani, Andrea; Ulzega, Simone; Manconi, Mauro,
(155), pp. 62-74.
Available from: https://doi.org/10.1016/j.cortex.2022.05.021
Delucchi, Matteo; Spinner, Georg; Scutari, Marco; Bijlenga, Philippe; Morel, Sandrine; Friedrich, Christoph M.; Furrer, Reinhard; Hirsch, Sven,
Computers in Biology and Medicine.
Available from: https://doi.org/10.1016/j.compbiomed.2022.105740
Juchler, Norman; Schilling, Sabine; Bijlenga, Philippe; Kurtcuoglu, Vartan; Hirsch, Sven,
Frontiers in Neurology.
Available from: https://doi.org/10.3389/fneur.2022.809391
Suter, Susanne; Spinner, Georg; Hoelz, Bianca; Rey, Sofia; Thanabalasingam, Sujeanthraa; Eckstein, Jens; Hirsch, Sven,
Available from: https://doi.org/10.21256/zhaw-24219