Discrete Bayesian Network Interpretation of the Cox’s Proportional Hazards Model

Kraisangka, Jidapa; Drużdżel, Marek J.

doi:10.1007/978-3-319-11433-0_16

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…This approach is especially useful when very little or no data are available. We assume that the CPH model’s assumptions are not violated and the risk factors or random variables X are time-independent discrete/binary variables [13].…”

Section: Bayesian Network Interpretation Of the Cph Modelmentioning

confidence: 99%

“…The model includes 19 binary risk factors (reproduced from the original paper in Table 1) and the baseline probability of survival, S 0 (1) = 0.9698. By following the method out-lined above, we created a BN-Cox model shown in Figure 3, which is equivalent to the CPH model reported in [13].…”

Section: Bayesian Network Interpretation Of the Cph Modelmentioning

confidence: 99%

“…In addition to the simplified, four-risk-factor model, we also created a complete Recidivism model with all eight risk factors (see details in [13]), seven of which are binary and one is categorical. The results were similar to those of the simplified models.…”

Section: Evaluation Of the Bn-cox Modelmentioning

confidence: 99%

“…It is currently a com-mercial product but is still available free of charge for academic research and teaching from BayesFusion, LLC, at https://www.bayesfusion.com/. Parts of this paper were presented at the 2014 and 2016 International Conferences on Probabilistic Graphical Models [13, 22]. The BN-Cox Interpretation of the PAH risk calculator, discussed in details in [20], was developed in collaboration with Dr. Raymond L. Benza of Allegheny General Hospital, Pittsburgh, PA.…”

Section: Acknowledgmentsmentioning

confidence: 99%

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A Bayesian network interpretation of the Cox's proportional hazard model

Kraisangka

Drużdżel

2018

International Journal of Approximate Reasoning

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Cox’s proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, Bayesian networks have become an attractive alternative with an increased modeling power and far broader applications. Our paper focuses on a Bayesian network interpretation of the CPH model (BN-Cox). We provide a method of encoding knowledge from existing CPH models in the process of knowledge engineering for Bayesian networks. This is important because in practice we often have CPH models available in the literature and no access to the original data from which they have been derived. We compare the accuracy of the resulting BN-Cox model to the original CPH model, Kaplan-Meier estimate, and Bayesian networks learned from data, including Naive Bayes, Tree Augmented Naive Bayes, Noisy-Max, and parameter learning by means of the EM algorithm. BN-Cox model came out as the most accurate of all BN approaches and very close to the original CPH model. We study two approaches for simplifying the BN-Cox model for the sake of representational and computational efficiency: (1) parent divorcing and (2) removing less important risk factors. We show that removing less important risk factors leads to smaller loss of accuracy.

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Section: Bayesian Network Interpretation Of the Cph Modelmentioning

confidence: 99%

Section: Bayesian Network Interpretation Of the Cph Modelmentioning

confidence: 99%

Section: Evaluation Of the Bn-cox Modelmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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A Bayesian network interpretation of the Cox's proportional hazard model

Kraisangka

Drużdżel

2018

International Journal of Approximate Reasoning

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“…However, it assumes that all sources are equally related and simply learns the parameters for each task independently. Kraisangka and Druzdzel (2014) construct BN parameters from a set of regression models used in survival analysis. However, this method cannot be generalized to transfer between BNs.…”

Section: Related Workmentioning

confidence: 99%

When and where to transfer for Bayesian network parameter learning

Zhou

Hospedales

Fenton

2016

Expert Systems with Applications

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Learning Bayesian networks from scarce data is a major challenge in real-world applications where data are hard to acquire. Transfer learning techniques attempt to address this by leveraging data from different but related problems. For example, it may be possible to exploit medical diagnosis data from a different country. A challenge with this approach is heterogeneous relatedness to the target, both within and across source networks. In this paper we introduce the Bayesian network parameter transfer learning (BNPTL) algorithm to reason about both network and fragment (sub-graph) relatedness. BNPTL addresses (i) how to find the most relevant source network and network fragments to transfer, and (ii) how to fuse source and target parameters in a robust way. In addition to improving target task performance, explicit reasoning allows us to diagnose network and fragment relatedness across BNs, even if latent variables are present, or if their state space is heterogeneous. This is important in some applications where relatedness itself is an output of interest. Experimental results demonstrate the superiority of BNPTL at various scarcities and source relevance levels compared to single task learning and other state-of-the-art parameter transfer methods. Moreover, we demonstrate successful application to real-world medical case studies.

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