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 Research Centre 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.
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.
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 ...
BISTOM - Bayesian Inference with Stochastic Models
In essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for making probabilistic predictions. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability ...
A cloud-based IoT approach for food safety and quality prediction
Safety and quality prediction are topical issues in food industry. We are developing a novel IoT approach in the framework of a collaboration with Genossenschaft Migros Zürich (GMZ), ZHAW (represented by the institutes IAS and ILGI) and Axino Solutions AG. The main goal of the project is to provide a robust, ...
PhD Network in Data Science
<>Swissuniversities project to promote PhD programs at ZHAW in the field of Data Science. <> <>
Reissenberger, Pamela; Serfözö, Peter; Piper, Diana; Juchler, Norman; Glanzmann, Sara; Gram, Jasmin; Hensler, Karina; Tonidandel, Hannah; Börlin, Elena; D’Souza, Marcus; Badertscher, Patrick; Eckstein, Jens,
Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial : detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor.
European Heart Journal - Digital Health.
Available from: https://doi.org/10.1093/ehjdh/ztad039
Bächinger, David; Filidoro, Noemi; Naville, Marc; Juchler, Norman; Kurtcuoglu, Vartan; Nadol, Joseph B.; Schuknecht, Bernhard; Kleinjung, Tobias; Veraguth, Dorothe; Eckhard, Andreas H.,
13(1), pp. 10303.
Available from: https://doi.org/10.1038/s41598-023-36479-5
Ulzega, Simone; Albert, Carlo; Beer, Jürg,
5th Swiss SCOSTEP Workshop, Windisch, Switzerland, 15-16 May 2023.
Bacci, Marco; Sukys, Jonas; Reichert, Peter; Ulzega, Simone; Albert, Carlo,
Stochastic Environmental Research and Risk Assessment.
Available from: https://doi.org/10.1007/s00477-023-02434-z
Ulzega, Simone; Albert, Carlo,
Hydrology and Earth System Sciences.
27(15), pp. 2935-2950.
Available from: https://doi.org/10.5194/hess-27-2935-2023