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Cancer Inform. 2014 Feb 13;13:47-57. doi: 10.4137/CIN.S13053. eCollection 2014.

A new method for predicting patient survivorship using efficient bayesian network learning.

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Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
Division of Hematology/Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.


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 individually. 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.


Bayesian network; Cox proportional hazard model; breast cancer; random survival forest; survivorship prediction

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