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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hilbe, J.: Survival analysis for epidemiologic and medical research. J. Stat. Softw. Book Rev. 30(4), 1–4 (2009)
Hommersom, A., Lucas, P.J.: Foundations of Biomedical Knowledge Representation. Springer, Heidelberg (2015)
Kraisangka, J., Druzdzel, M.J.: Discrete Bayesian network interpretation of the Cox’s proportional hazards model. In: Gaag, L.C., Feelders, A.J. (eds.) PGM 2014. LNCS, vol. 8754, pp. 238–253. Springer, Cham (2014). doi:10.1007/978-3-319-11433-0_16
Kraisangka, J., Druzdzel, M.J.: Making large Cox’s proportional hazard models tractable in Bayesian networks. In: Probabilistic Graphical Models (2016)
Ostrom, Q.T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., Stroup, N.E., Kruchko, C., Barnholtz-Sloan, J.S.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the united states in 2006–2010. Neuro-oncology 15(Suppl. 2), ii1–ii56 (2013)
Scutari, M.: Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35(3), 1–22 (2010)
Verduijn, M., Peek, N., Rosseel, P.M., de Jonge, E., de Mol, B.A.: Prognostic Bayesian networks: I: rationale, learning procedure, and clinical use. J. Biomed. Inform. 40(6), 609–618 (2007)
Weeks, J., Cook, E.F., O’Day, S., Peterson, L., Wenger, N., Reding, D., Harrel, F., Kussin, P., Dawson, N., Connors, A., Lynn, J., Phillips, R.: Relationship between cancer patients’ predictions of prognosis and their treatment preferences. J. Am. Med. Assoc. 279(21), 1709–1714 (1998)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Rabinowicz, S., Hommersom, A., Butz, R., Williams, M. (2017). A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks. In: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-59758-4_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59757-7
Online ISBN: 978-3-319-59758-4
eBook Packages: Computer ScienceComputer Science (R0)