Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

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...

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Bibliographic Details
Main Authors:	Bijlenga, P. (Author), Delucchi, M. (Author), Friedrich, C.M (Author), Furrer, R. (Author), Hirsch, S. (Author), Morel, S. (Author), Scutari, M. (Author), Spinner, G.R (Author)
Format:	Article
Language:	English
Published:	Elsevier Ltd 2022
Subjects:	Additives Aneurysm rupture Bayesia n networks Bayesian network Bayesian network models Bayesian networks Causal relationships Clinical decision making Correlation methods Decision making Diagnosis Graphic methods Intracranial aneurysm Intracranial aneurysms Markov processes Mixed data Probabilistic graphical model Probabilistic graphical models Regression analysis Risk assessment Risk factors Rupture risk
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Bayesian network analysis reveals the interplay of intracranial aneurysm rupture risk factors

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