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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25226
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dc.contributor.authorDelucchi, Matteo-
dc.contributor.authorSpinner, Georg-
dc.contributor.authorScutari, Marco-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorMorel, Sandrine-
dc.contributor.authorFriedrich, Christoph M.-
dc.contributor.authorFurrer, Reinhard-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2022-06-30T15:21:42Z-
dc.date.available2022-06-30T15:21:42Z-
dc.date.issued2022-06-20-
dc.identifier.issn0010-4825de_CH
dc.identifier.issn1879-0534de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/25226-
dc.description.abstractClinical decision making regarding the treatment of unruptured intracranial aneurysms (IA) benefits from a better understanding of the interplay of IA rupture risk factors. Probabilistic graphical models can capture and graphically display potentially causal relationships in a mechanistic model. In this study, Bayesian networks (BN) were used to estimate IA rupture risk factors influences. From 1248 IA patient records, a retrospective, single-cohort, patient-level data set with 9 phenotypic rupture risk factors (n=790 complete entries) was extracted. Prior knowledge together with score-based structure learning algorithms estimated rupture risk factor interactions. Two approaches, discrete and mixed-data additive BN, were implemented and compared. The corresponding graphs were learned using non-parametric bootstrapping and Markov chain Monte Carlo, respectively. The BN models were compared to standard descriptive and regression analysis methods. Correlation and regression analyses showed significant associations between IA rupture status and patient’s sex, familial history of IA, age at IA diagnosis, IA location, IA size and IA multiplicity. BN models confirmed the findings from standard analysis methods. More precisely, they directly associated IA rupture with familial history of IA, IA size and IA location in a discrete framework. Additive model formulation, enabling mixed-data, found that IA rupture was directly influenced by patient age at diagnosis besides additional mutual influences of the risk factors. This study establishes a data-driven methodology for mechanistic disease modelling of IA rupture and shows the potential to direct clinical decision-making in IA treatment, allowing personalised prediction.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofComputers in Biology and Medicinede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectIntracranial aneurysmde_CH
dc.subjectProbabilistic graphical modelde_CH
dc.subjectBayesian networkde_CH
dc.subject.ddc003: Systemede_CH
dc.subject.ddc362.11: Krankenhäuser und verwandte Einrichtungende_CH
dc.titleBayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factorsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1016/j.compbiomed.2022.105740de_CH
dc.identifier.doi10.21256/zhaw-25226-
zhaw.funding.euNode_CH
zhaw.issue105740de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume147de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedMedical Image Analysis & Data Modelingde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.webfeedHealth Research Hub (LSFM)de_CH
zhaw.webfeedDatalabde_CH
zhaw.funding.zhawDatengetriebene Entscheidungsunterstützung bei intrakraniellen Aneurysmen und in der Spitalgastronomie mittels Bayes'schen Netzwerkende_CH
zhaw.funding.zhawPhD Network in Data Sciencede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
zhaw.relation.referenceshttps://github.com/hirsch-lab/bnaiaR/releases/tag/v1.0de_CH
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Delucchi, M., Spinner, G., Scutari, M., Bijlenga, P., Morel, S., Friedrich, C. M., Furrer, R., & Hirsch, S. (2022). Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Computers in Biology and Medicine, 147(105740). https://doi.org/10.1016/j.compbiomed.2022.105740
Delucchi, M. et al. (2022) ‘Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors’, Computers in Biology and Medicine, 147(105740). Available at: https://doi.org/10.1016/j.compbiomed.2022.105740.
M. Delucchi et al., “Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors,” Computers in Biology and Medicine, vol. 147, no. 105740, Jun. 2022, doi: 10.1016/j.compbiomed.2022.105740.
DELUCCHI, Matteo, Georg SPINNER, Marco SCUTARI, Philippe BIJLENGA, Sandrine MOREL, Christoph M. FRIEDRICH, Reinhard FURRER und Sven HIRSCH, 2022. Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors. Computers in Biology and Medicine. 20 Juni 2022. Bd. 147, Nr. 105740. DOI 10.1016/j.compbiomed.2022.105740
Delucchi, Matteo, Georg Spinner, Marco Scutari, Philippe Bijlenga, Sandrine Morel, Christoph M. Friedrich, Reinhard Furrer, and Sven Hirsch. 2022. “Bayesian Network Analysis Reveals the Interplay of Intracranial Aneurysm Rupture Risk Factors.” Computers in Biology and Medicine 147 (105740). https://doi.org/10.1016/j.compbiomed.2022.105740.
Delucchi, Matteo, et al. “Bayesian Network Analysis Reveals the Interplay of Intracranial Aneurysm Rupture Risk Factors.” Computers in Biology and Medicine, vol. 147, no. 105740, June 2022, https://doi.org/10.1016/j.compbiomed.2022.105740.


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