Bayesian updating in causal probabilistic networks by local computations

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Janssen KJM, Donders AR, Harrell FE, Vergouwe Y, Chen Q, et al.

Potthoff RF, Tudor GE, Pieper KS, Hasselblad V (2006) Can one assess whether missing data are missing at random in medical studies?

Stojadinovic A, Peoples GE, Libutti SK, Henry LR, Eberhardt J et al.

However, this percentage rises to 76% when partial matches are included. Holmberg L, Sandin F, Bray F, Richards M, Spicer J et al. Accessed 22 February 2013 doi:10.1136/thx.2009.124222. (2010) A Bayesian network approach to feature selection in mass spectrometry data. Kuschner KW, Malyarenko DI, Cooke WE, Cazares LH, Semmes OJ et al. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Lavrac N (1999) Selected techniques for data mining in medicine.

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