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

Jiang X, Xue D, Brufsky A, Khan S, Neapolitan R - Cancer Inform (2014)

Bottom Line: BNs have excellent architecture for decision support systems.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Models learned by EBMC_S for 1, 5, 10, and 15 year predictions.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3928477&req=5

f6-cin-13-2014-047: Models learned by EBMC_S for 1, 5, 10, and 15 year predictions.

Mentions: Figure 6 shows the models studied by EBMC_S for 1, 5, 10, and 15 year predictions, when using the entire dataset to study the model. We see that the predictors for the various years are similar but not identical. It is notable that age is a predictor only for long-term survival. It is not surprising that age predicts the long-term survival since we are modeling all-cause mortality; however, it is interesting that age does not seem to predict short-term survival. These results indicate obtaining a separate prediction model for each year individually has advantages in the patient survival prediction problem over models that ascertain a global prediction model for all years.


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

Jiang X, Xue D, Brufsky A, Khan S, Neapolitan R - Cancer Inform (2014)

Models learned by EBMC_S for 1, 5, 10, and 15 year predictions.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3928477&req=5

f6-cin-13-2014-047: Models learned by EBMC_S for 1, 5, 10, and 15 year predictions.
Mentions: Figure 6 shows the models studied by EBMC_S for 1, 5, 10, and 15 year predictions, when using the entire dataset to study the model. We see that the predictors for the various years are similar but not identical. It is notable that age is a predictor only for long-term survival. It is not surprising that age predicts the long-term survival since we are modeling all-cause mortality; however, it is interesting that age does not seem to predict short-term survival. These results indicate obtaining a separate prediction model for each year individually has advantages in the patient survival prediction problem over models that ascertain a global prediction model for all years.

Bottom Line: BNs have excellent architecture for decision support systems.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.

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.

No MeSH data available.


Related in: MedlinePlus