<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Antal</style></author><author><style face="normal" font="default" size="100%">T. Mészáros</style></author><author><style face="normal" font="default" size="100%">B. De Moor</style></author><author><style face="normal" font="default" size="100%">T.P. Dobrowiecki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Annotated Bayesian networks: a tool to integrate textual and probabilistic medical knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">{Computer-Based} Medical Systems, 2001. {CBMS} 2001. Proceedings. 14th {IEEE} Symposium on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">annotated Bayesian network</style></keyword><keyword><style  face="normal" font="default" size="100%">belief networks</style></keyword><keyword><style  face="normal" font="default" size="100%">benign ovarian masses</style></keyword><keyword><style  face="normal" font="default" size="100%">clinician personal textual information</style></keyword><keyword><style  face="normal" font="default" size="100%">decision support</style></keyword><keyword><style  face="normal" font="default" size="100%">decision support systems</style></keyword><keyword><style  face="normal" font="default" size="100%">dedicated representation</style></keyword><keyword><style  face="normal" font="default" size="100%">domain model</style></keyword><keyword><style  face="normal" font="default" size="100%">explanation</style></keyword><keyword><style  face="normal" font="default" size="100%">malignant ovarian masses</style></keyword><keyword><style  face="normal" font="default" size="100%">medical background knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">medical expert systems</style></keyword><keyword><style  face="normal" font="default" size="100%">medical information systems</style></keyword><keyword><style  face="normal" font="default" size="100%">patient data</style></keyword><keyword><style  face="normal" font="default" size="100%">personalized explanation</style></keyword><keyword><style  face="normal" font="default" size="100%">pre-operative discrimination</style></keyword><keyword><style  face="normal" font="default" size="100%">probabilistic medical knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">probabilistic semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">semantic network</style></keyword><keyword><style  face="normal" font="default" size="100%">textual information sources</style></keyword><keyword><style  face="normal" font="default" size="100%">textual medical knowledge</style></keyword><keyword><style  face="normal" font="default" size="100%">traceability</style></keyword><keyword><style  face="normal" font="default" size="100%">tumours</style></keyword><keyword><style  face="normal" font="default" size="100%">uncertainty handling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">177–182</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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</style></abstract></record></records></xml>