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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

A BN modeling the relationships among a small subset of variables related to respiratory diseases.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3928477&req=5

f1-cin-13-2014-047: A BN modeling the relationships among a small subset of variables related to respiratory diseases.

Mentions: Figure 1 shows a causal BN modeling the relationships among a small subset of variables related to respiratory diseases. The value h1 indicates that the patient has a smoking history and the value h2 indicates the patient does not. The other values have similar meaning.


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)

A BN modeling the relationships among a small subset of variables related to respiratory diseases.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-13-2014-047: A BN modeling the relationships among a small subset of variables related to respiratory diseases.
Mentions: Figure 1 shows a causal BN modeling the relationships among a small subset of variables related to respiratory diseases. The value h1 indicates that the patient has a smoking history and the value h2 indicates the patient does not. The other values have similar meaning.

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