Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25226
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors
Authors: Delucchi, Matteo
Spinner, Georg
Scutari, Marco
Bijlenga, Philippe
Morel, Sandrine
Friedrich, Christoph M.
Furrer, Reinhard
Hirsch, Sven
et. al: No
DOI: 10.1016/j.compbiomed.2022.105740
10.21256/zhaw-25226
Published in: Computers in Biology and Medicine
Volume(Issue): 147
Issue: 105740
Issue Date: 20-Jun-2022
Publisher / Ed. Institution: Elsevier
ISSN: 0010-4825
1879-0534
Language: English
Subjects: Intracranial aneurysm; Probabilistic graphical model; Bayesian network
Subject (DDC): 003: Systems
362.11: Hospitals and related institutions
Abstract: Clinical 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.
URI: https://digitalcollection.zhaw.ch/handle/11475/25226
Related research data: https://github.com/hirsch-lab/bnaiaR/releases/tag/v1.0
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
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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