@conference {antal_domain_2002,
	title = {Domain knowledge based information retrieval language: an application of annotated Bayesian networks in ovarian cancer domain},
	booktitle = {{Computer-Based} Medical Systems, 2002. {(CBMS} 2002). Proceedings of the 15th {IEEE} Symposium on},
	year = {2002},
	pages = {213{\textendash}218},
	abstract = {The increasing amount and variety of domain knowledge and the availability of increasingly large quantities of electronic literature requires new types of support for the development of complex knowledge models. P. Antal et al. (2001) proposed the application of so-called annotated Bayesian networks {(ABNs),} which are textually-enriched probabilistic domain models that help knowledge engineers and medical experts to find and organize the information that is necessary in model-building. In this paper, we describe an information retrieval language in which the formalized domain knowledge and the attached textual information can be accessed in an integrated fashion and can be used to define various retrieval schemes and relevance measures. This language on the one hand provides maximum flexibility for knowledge engineers to exploit the available annotated domain model as contextual information. On the other hand, it allows the definition of complex, high-level queries, in which the contextual use of the annotated domain model can be optimized for clinical situations. We compare the performance of the standard and the proposed query language in the ovarian cancer domain.},
	keywords = {annotated Bayesian network, annotated domain model, attached textual information, belief networks, cancer, clinical situations, complex high-level queries, complex knowledge model development, contextual information, contextual use optimization, deductive databases, domain knowledge-based information retrieval language, electronic literature, electronic publishing, formalized domain knowledge, gynaecology, information finding, information organization, information relevance measures, knowledge engineering, medical expert systems, medical experts, medical information systems, model building, ovarian cancer, performance, query language, relevance feedback, textually enriched probabilistic domain models},
	isbn = {1063-7125},
	doi = {{10.1109/CBMS.2002.1011379}},
	author = {P. Antal and B. De Moor and D. Timmerman and T. M{\'e}sz{\'a}ros and T.P. Dobrowiecki}
}
@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}
}
