Publication type: Conference paper
Type of review: Peer review (abstract)
Title: Bayesian networks to disentangle the interplay of intracranial aneurysm rupture risk factors
Authors: Delucchi, Matteo
Spinner, Georg Ralph
Scutari, Marco
Bijlenga, Philippe
Morel, Sandrine
Friedrich, Christoph M.
Hirsch, Sven
et. al: No
Proceedings: CMBE 2022 : 7th International Conference on Computational & Mathematical Biomedical Engineering
Editors of the parent work: Nithiarasu, Perumal
Vergara, Christian
Volume(Issue): 2022
Issue: 1
Page(s): 22
Pages to: 25
Conference details: 7th International Conference on Computational and Mathematical Biomedical Engineering (CMBE22), Milan, Italy, 27-29 June 2022
Issue Date: 2022
Publisher / Ed. Institution: Computational and scientific consultancy services
Publisher / Ed. Institution: United Kingdom
ISBN: 978-0-9562914-6-2
ISSN: 2227-9385
Language: English
Subjects: Intracranial aneurysm; Probabilistic graphical model; Bayesian network
Subject (DDC): 003: Systems
362.11: Hospitals and related institutions
Abstract: Various intracranial aneurysm (IA) rupture risk factors are used for risk assessment, but little is understood about their complex interactions leading to IA rupture. In this study, Bayesian networks (BN) were learned on data of nine risk factors from 790 patients and compared to standard descriptive and regression analyses. The results from standard methods agreed with previous studies and could be extended with BNs by uncovering additional associations in the data. The graphical representation of the IA rupture risk factors in BNs facilitated the result understanding and provided new hypotheses on the risk factor interdependencies.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: Datengetriebene Entscheidungsunterstützung bei intrakraniellen Aneurysmen und in der Spitalgastronomie mittels Bayes'schen Netzwerken
PhD Network in Data Science
Appears in collections:Publikationen Life Sciences und Facility Management

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