Abstract
The formalization of clinical guideline knowledge is a prerequisite for the development of guideline-based decision support tools that can be used in clinical practice. Several guideline representation languages have been developed to formalize clinical guidelines and execute them over individual patient data. However, no standard has emerged from these efforts, and the core guideline elements to be represented have not been agreed upon in practice. One result is that there is little support when it comes to re-engineer a guideline modelled in a specific language into another language with different features. In this paper we describe a practical exercise consisting in modelling a guideline fragment in a target representation language starting from the same fragment modelled in a source language, having the source and target languages very different features. Concretely, we used PROforma as the source language and GDL as the target one. We also describe a methodological approach to facilitate this task. The lessons learnt from this work can be of interest not only to modellers tackling a similar task but also to developers of guideline transformation methods.
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Acknowledgements
This work has been supported by the Spanish Ministry of Economy and Competitiveness and the ERDF (European Regional Development Fund) through grant TIN2014-53749-C2-1-R, and by the Ministry of Education, Culture and Sports through grant PRX18/00350.
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Marcos, M., Campos, C., Martínez-Salvador, B. (2019). A Practical Exercise on Re-engineering Clinical Guideline Models Using Different Representation Languages. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_1
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DOI: https://doi.org/10.1007/978-3-030-37446-4_1
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