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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

ROC curves for 1, 5, 10, and 15 year predictions.
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Related In: Results  -  Collection


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f4-cin-13-2014-047: ROC curves for 1, 5, 10, and 15 year predictions.

Mentions: Figure 4 shows the ROC curves for EBMC_S for predictions at 1, 5, 10, and 15 years; and Figure 5 shows the AUROCs for all 15 years plotted as a function of the year. We see that, in general, prediction improves as the number of years into the future increases. The result is initially unintuitive because ordinarily we would expect to be able to predict closer events better than more distant events. However, in the case of breast cancer survival, it seems that we can predict whether the person will survive the cancer (15 year prediction) fairly well, but we cannot as readily predict how long those who do not survive the cancer will live.


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)

ROC curves 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

f4-cin-13-2014-047: ROC curves for 1, 5, 10, and 15 year predictions.
Mentions: Figure 4 shows the ROC curves for EBMC_S for predictions at 1, 5, 10, and 15 years; and Figure 5 shows the AUROCs for all 15 years plotted as a function of the year. We see that, in general, prediction improves as the number of years into the future increases. The result is initially unintuitive because ordinarily we would expect to be able to predict closer events better than more distant events. However, in the case of breast cancer survival, it seems that we can predict whether the person will survive the cancer (15 year prediction) fairly well, but we cannot as readily predict how long those who do not survive the cancer will live.

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