On the potential of domain literature for clustering and Bayesian network learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using literature and data to learn Bayesian networks as clinical models of ovarian tumors
Artificial Intelligence in Medicine
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Abstract: In previous publications we have reported on the development of Bayesian Network models for the preoperative discrimination between malignant and benign ovarian mass. The models incorporated both medical background knowledge and patient data, which required the traceability of the incorporated prior medical knowledge. For this purpose, we followed a particular annotation method for Bayesian Networks using a dedicated representation. In this paper we present the resulting Annotated Bayesian Network (ABN) representation that consists of a regular Bayesian Network with standard probabilistic semantics and a corresponding semantic network, to which the textual information sources are attached. We demonstrate the applicability of such dual model to represent both the rigorous probabilistic and the unconstrained textual medical knowledge. We describe methods on how these Annotated Bayesian Network models can be used: (1) as a domain model to arrange the personal textual information of the clinician according to the semantics of the domain , (2) in decision support to provide detailed, even personalized explanation, and (3) to enhance the information retrieval to find new textual information more efficiently.