Research Centre for Computational Health
The Computational Health Research Group uses data-driven and mechanistic modelling to address issues in medicine and biology. Key tools include machine learning for analysing images and signals, graphical networks, estimating parameters for systems of differential equations and simulating physiology.
About us
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.
Biomedical 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
Medical Image Analysis
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.
Biosignal Analysis & Digital Health
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
Software & Code
We provide code and projects developed by the Research Centre for Computational Health on our GitHub organization page. 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.
Teaching Activities
The courses offered by the centre of Computational Health cover topics in mathematical and physical modeling. At Bachelor level it is responsible for the specialization Digital Health. The specialization teaches physics and mathematics for other BSc programs at the department. In the MSc Applied Computational Life Sciences and the PhD program Data Science advanced courses in modeling of complex systems are taught.
Projects
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Delirium DynamiX (DDX)
Delirium DynamiX harnesses high-resolution USZ ICU data with on-prem LLMs and novel time-series Additive Bayesian Networks. This interpretable modeling of dynamic causal chains enables / earlier delirium detection, / targeted interventions, external validation, / a co-designed dashboard, and / a…
current, 06/2026 - 05/2029
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IVIM: muscle activation of the shoulder
Analysis of IVIM MRI data from the SCMI of Balgrist Campus to measure muscle activation via perfusion after clinical movement tests.
expired, 08/2023 - 07/2024
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Stroke DynamiX – Graphical and Causal Networks for Personalized Stroke Management
Stroke DynamiX explores data-driven stroke management using machine-learning techniques in a consortium of statistics researchers, translational enablers, and clinical partners. We implement statistical tools to dynamically model stroke epidemiologically and predict real-time sepsis onset in the…
expired, 05/2023 - 04/2026
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Modeling of multicentric and dynamic stroke health data
The aim of the project is the further development of probabilistic and dynamic modeling of diseases in the form of Bayesian networks in the field of digital health as a central strategic pillar of the newly formed specialist group "Medical Image Analysis and Data Modeling" of the "Computational…
expired, 01/2023 - 12/2024
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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…
expired, 03/2020 - 12/2021
Publications
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Detmer, Felicitas J.; Mut, Fernando; Slawski, Martin; Hirsch, Sven; Bijlenga, Philippe; Cebral, Juan R.,
2020.
Acta Neurochirurgica.
162(3), pp. 553-566.
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Watanabe, Kazuhiro; Anzai, Hitomi; Juchler, Norman; Hirsch, Sven; Ohta, Makoto,
2020.
In:
International Mechanical Engineering Congress and Exposition : Volume 3 - biomedical and biotechnology engineering.
2019 International Mechanical Engineering Congress and Exposition, IMECE2019, Salt Lake City, Utah, USA, 11-14 November 2019.
The American Society of Mechanical Engineers.
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Detmer, Felicitas J.; Lückehe, Daniel; Mut, Fernando; Slawski, Martin; Hirsch, Sven; Bijlenga, Philippe; von Voigt, Gabriele; Cebral, Juan R.,
2020.
Comparison of statistical learning approaches for cerebral aneurysm rupture assessment.
International Journal of Computer Assisted Radiology and Surgery.
15(1), pp. 141-150.
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Borčinová, Martina; Raschmanová, Hana; Zamora, Iwo; Looser, Verena; Marešová, Helena; Hirsch, Sven; Kyslík, Pavel; Kovar, Karin,
2020.
Production and secretion dynamics of prokaryotic Penicillin G acylase in Pichia pastoris.
Applied Microbiology and Biotechnology.
104(13), pp. 5787-5800.
Available from: https://doi.org/10.21256/zhaw-21535
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Albert, Carlo; Ulzega, Simone,
2020.
Stochastic resonance could explain recurrence of Grand Minima.
In:
EGU General Assembly 2020, online, 4-8 May 2020.
