@conference {antal_annotated_2001,
	title = {Annotated Bayesian networks: a tool to integrate textual and probabilistic medical knowledge},
	booktitle = {{Computer-Based} Medical Systems, 2001. {CBMS} 2001. Proceedings. 14th {IEEE} Symposium on},
	year = {2001},
	pages = {177{\textendash}182},
	abstract = {We have previously (2000) reported on the development of Bayesian network models for the pre-operative discrimination between malignant and benign ovarian masses. 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 textual information sources are attached. We demonstrate the applicability of such a dual model to represent both the rigorous probabilistic and the unconstrained textual medical knowledge. We describe methods on how these {ABN} models can be used: (1) as a domain model to arrange the personal textual information of a clinician according to the semantics of the domain, (2) in decision support to provide detailed (and even personalized) explanation, and (3) to enhance the information retrieval to find new textual information more efficiently},
	keywords = {annotated Bayesian network, belief networks, benign ovarian masses, clinician personal textual information, decision support, decision support systems, dedicated representation, domain model, explanation, malignant ovarian masses, medical background knowledge, medical expert systems, medical information systems, patient data, personalized explanation, pre-operative discrimination, probabilistic medical knowledge, probabilistic semantics, semantic network, textual information sources, textual medical knowledge, traceability, tumours, uncertainty handling},
	doi = {{10.1109/CBMS.2001.941717}},
	author = {P. Antal and T. M{\'e}sz{\'a}ros and B. De Moor and T.P. Dobrowiecki}
}
