title: A new method for predicting patient survivorship using efficient bayesian network learning.
abstract: 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.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Diyang
authors.initials: D
authors.lastname: Xue
authors.affiliation: Division of Hematology/Oncology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Brufsky
authors.affiliation: Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
authors.forename: Seema
authors.initials: S
authors.lastname: Khan
authors.affiliation: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
authors.forename: Richard
authors.initials: R
authors.lastname: Neapolitan
keywords: Bayesian network; Cox proportional hazard model; breast cancer; random survival forest; survivorship prediction