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Studying the Reuse of Content in Biomedical Ontologies: An Axiom-Based Approach Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks

Authors : Simon Rabinowicz, Arjen Hommersom, Raphaela Butz, Matt Williams

Published in: Artificial Intelligence in Medicine

Publisher: Springer International Publishing

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Abstract

Bayesian networks are attractive for developing prognostic models in medicine, due to the possibility for modelling the multivariate relationships between variables that come into play in the care process. In practice, the development of these models is hindered due to the fact that medical data is often censored, in particular the survival time. In this paper, we propose to directly integrate Cox proportional hazards models as part of a Bayesian network. Furthermore, we show how such Bayesian network models can be learned from data, after which these models can be used for probabilistic reasoning about survival. Finally, this method is applied to develop a prognostic model for Glioblastoma Multiforme, a common malignant brain tumour.

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Metadata
Title
A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks
Authors
Simon Rabinowicz
Arjen Hommersom
Raphaela Butz
Matt Williams
Copyright Year
2017
Book
Artificial Intelligence in Medicine

Print ISBN: 978-3-319-59757-7

Electronic ISBN: 978-3-319-59758-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59758-4_9

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