A new method for predicting patient survivorship using efficient Bayesian network learning

Xia Jiang, Diyang Xue, Adam Brufsky, Seema Khan, Richard Neapolitan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The purpose of this investigation is to develop and evaluate a new Bayesian network (BN)-based patient survivorship prediction method. The central hypothesis is that the method predicts patient survivorship well, while having the capability to handle high-dimensional data and be incorporated into a clinical decision support system (CDSS). We have developed EBMC_Survivorship (EBMC_S), which predicts survivorship for each year individu-ally. EBMC_S is based on the EBMC BN algorithm, which has been shown to handle high-dimensional data. BNs have excellent architecture for decision support systems. In this study, we evaluate EBMC_S using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which concerns breast tumors. A 5-fold cross-validation study indicates that EMBC_S performs better than the Cox proportional hazard model and is comparable to the random survival forest method. We show that EBMC_S provides additional information such as sensitivity analyses, which covariates predict each year, and yearly areas under the ROC curve (AUROCs). We conclude that our investigation supports the central hypothesis.

Original languageEnglish (US)
Pages (from-to)47-57
Number of pages11
JournalCancer Informatics
StatePublished - Feb 13 2014


  • Bayesian network
  • Breast cancer
  • Cox proportional hazard model
  • Random survival forest
  • Survivorship prediction

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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