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A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks

  • Simon Rabinowicz
  • Arjen HommersomEmail author
  • Raphaela Butz
  • Matt Williams
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

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.

Keywords

Bayesian Network Glioblastoma Multiforme Prognostic Model Karnofsky Performance Score Survival Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Simon Rabinowicz
    • 1
  • Arjen Hommersom
    • 2
    • 3
    Email author
  • Raphaela Butz
    • 2
    • 4
  • Matt Williams
    • 5
    • 6
  1. 1.Faculty of MedicineImperial College LondonLondonUK
  2. 2.Department of Computer ScienceOpen UniversityHeerlenThe Netherlands
  3. 3.Department of Software ScienceRadboud UniversityNijmegenThe Netherlands
  4. 4.Institute for Computer ScienceTH KölnCologneGermany
  5. 5.Department of RadiotherapyCharing Cross HospitalLondonUK
  6. 6.Computational Oncology LaboratoryImperial College LondonLondonUK

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