Stroke DynamiX
Graphical and Causal Networks for Personalized Stroke Management
Description
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 clinic.
Key Data
Projectlead
Deputy Projectlead
Co-Projectlead
Prof. Dr. Reinhard Furrer, Dr. Zsolt Kulcsar
Project team
Project partners
Universitätsspital Zürich; Hôpitaux universitaires de Genève
Project status
ongoing, started 05/2023
Funding partner
Kanton Zürich / Digitalisierungsinitiative DIZH
Project budget
599'866 CHF
Further documents and links
Publications
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Additive Bayesian Networks
2024 Delucchi, Matteo; Liechti, Jonas I.; Spinner, Georg; Furrer, Reinhard
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An explainable multicentric analysis for understanding the aetiology of intracranial aneurysm disease
2023 Delucchi, Matteo; Spinner, Georg R.; Bijlenga, Philippe; Morel, Sandrine; Hostettler, Isabel; Werring, David; Wostrack, Maria; Meyer, Bernhard; Bourcier, Romain; Lindgren, Antti; Bakker, Mark K.; Ruigrok, Ynte M.; Furrer, Reinhard; Hirsch, Sven
