© 2008 Aten et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

Abstract not found

In biomedical domains, free text electronic literature is an important resource for knowledge discovery and acquisition, particularly to provide a priori components for evaluating or learning domain models. Aiming at the automated extraction of this prior knowledge we discuss the types of uncertainties in a domain with respect to causal mechanisms, formulate assumptions about their report in scientific papers and derive generative probabilistic models for the occurrences of biomedical concepts in papers. These results allow the discovery and extraction of latent causal dependency relations from the domain literature using minimal linguistic support. Contrary to the currently prevailing methods, which assume that relations are sufficiently formulated for linguistic methods, our approach assumes only the report of causally associated entities without their tentative status or relations, and can discover new relations and prune redundancies by providing a domain-wide model. Therefore the proposed Bayesian network based text mining is an important complement to the linguistic approaches. 1

We present two Bayesian algorithms CD-B and CD-H for discovering unconfounded cause and effect relationships from observational data without assuming causal sufficiency which precludes hidden common causes for the observed variables. The CD-B algorithm first estimates the Markov blanket of a node X using a Bayesian greedy search method and then applies Bayesian scoring methods to discriminate the parents and children of X. Using the set of parents and set of children CD-B constructs a global Bayesian network and outputs the causal effects of a node X based on the identification of Y arcs. Recall that if a node X has two parent nodes A,B and a child node C such that there is no arc between A,B and A,B are not parents of C, then the arc from X to C is called a Y arc. The CD-H algorithm uses the MMPC algorithm to estimate the union of parents and children of a target node X. The subsequent steps are similar to those of CD-B. We evaluated the CD-B and CD-H algorithms empirically based on simulated data from four different Bayesian networks. We also present comparative results based on the identification of Y structures and Y arcs from the output of the PC, MMHC and FCI algorithms. The results appear promising for mining causal relationships that are unconfounded by hidden variables from observational data.

This paper presents a semi-automatic approach for confounding-aware subgroup discovery: Confounding essentially disturbs the measured effect of an association between variables due to the influence of other parameters that were not considered. The proposed method is embedded into a general subgroup discovery approach, and provides the means for detecting potentially confounded subgroup patterns, other unconfounded relations, and/or patterns that are affected by effect-modification. Since there is no purely automatic test for confounding, the discovered relations are presented to the user in a semiautomatic approach. Furthermore, we utilize (causal) domain knowledge for improving the results of the algorithm, since confounding is itself a causal concept. The applicability and benefit of the presented technique is illustrated by real-world examples from a case-study in the medical domain. 1.