Agents and Clinical Guidelines: Filling the Semantic Gap

Authors:
David Isern;David Sánchez;Antonio Moreno
Affiliations:
iTAKA Res. Grp. --Intell. Tech. for Adv. Knowl. Acq., Univ. Rovira i Virgili. Dept. of Comp. Sci. and Math., Avda. Paisos Catalans, 26. 43007 Tarragona, Catalunya, {david.isern, david.sanchez, ant ...;iTAKA Res. Grp. --Intell. Tech. for Adv. Knowl. Acq., Univ. Rovira i Virgili. Dept. of Comp. Sci. and Math., Avda. Paisos Catalans, 26. 43007 Tarragona, Catalunya, {david.isern, david.sanchez, ant ...;iTAKA Res. Grp. --Intell. Tech. for Adv. Knowl. Acq., Univ. Rovira i Virgili. Dept. of Comp. Sci. and Math., Avda. Paisos Catalans, 26. 43007 Tarragona, Catalunya, {david.isern, david.sanchez, ant ...
Venue:
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Year:
2007

Citing 7
Cited 1

Enabling technology for knowledge sharing

AI Magazine
Toward a representation format for sharable clinical guidelines

Computers and Biomedical Research
Ontological Engineering

Ontological Engineering
Swoogle: a search and metadata engine for the semantic web

Proceedings of the thirteenth ACM international conference on Information and knowledge management
Extraction and use of linguistic patterns for modelling medical guidelines

Artificial Intelligence in Medicine
Using aggregation operators to personalize agent-based medical services

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Guideline-based careflow systems

Artificial Intelligence in Medicine

A semantically enriched clinical guideline model enabling deployment in heterogeneous healthcare environments

IEEE Transactions on Information Technology in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Medical ontologies are developed to solve problems such as the demand for reusing, sharing and transmitting data. The unambiguous communication of complex and detailed medical concepts is a crucial feature in current medical information systems. In these systems, several agents must interact in order to share their results and, thus, they must use a medical terminology with a clear and non-confusing meaning. The paper presents the inclusion of an especially designed medical ontology in the HECASE2 multi-agent system. HECASE2 has been developed to help doctors in applying clinical guidelines to their patients in a semi-automatic fashion. In addition, it shows how intelligent agents may take profit from the modelled medical knowledge to coordinate their activities in the enactment of clinical guidelines.