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Authors: Nekane Larburu 1 ; Naiara Muro 1 ; Iván Macía 1 ; Eider Sánchez 2 ; Hui Wang 3 ; John Winder 3 ; Jacques Boaud 4 and Brigitte Séroussi 5

Affiliations: 1 Vicomtech-IK4 and Biodonostia, Spain ; 2 NARU, Spain ; 3 Ulster University, United Kingdom ; 4 AP-HP, DRCD, Sorbonne Universités, UPMC Univ. Paris 06, INSERM and Université Paris 13, France ; 5 Sorbonne Universités, UPMC Univ. Paris 06, INSERM, Université Paris 13, AP-HP, Hôpital Tenon and DSP, France

ISBN: 978-989-758-213-4

Keyword(s): Evidence Based Medicine, Breast Cancer, Computer Interpretable Clinical Guidelines, CDSS.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Information Systems Analysis and Specification ; Knowledge Management ; Knowledge-Based Systems ; Ontologies and the Semantic Web ; Practice-based Research Methods for Healthcare IT ; Society, e-Business and e-Government ; Symbolic Systems ; Web Information Systems and Technologies

Abstract: Over the past years, clinical guidelines have increasingly become part of the clinical daily practice in order to provide best available Evidence-Based-Medicine services. Hence, their formalization as computer interpretable guidelines (CIG) and their implementation in clinical decision support systems (CDSSs) are emerging to support clinicians in their decision making process and potentially improve medical outcomes. However, guideline compliancy in the clinical daily practice is still “low”. Some of the reasons for such low compliance rate are (i) lack of a complete guideline to cover special clinical cases (e.g. oncogeriatric cases), (ii) absence of parameters that current guidelines do not consider (e.g. lifestyle) and (iii) absence of up-to-date guidelines due to lengthy validation procedures. In this paper we present a novel method to build a CDSS that, besides integrating CIGs, stores experts’ knowledge to enrich the CDSS and provide best support to clinicians. The knowledge inc ludes new evidence collected over time by the systematic usage of CDSSs. (More)

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Paper citation in several formats:
Larburu N., Muro N., Macía I., Sánchez E., Wang H., Winder J., Boaud J. and Séroussi B. (2017). Augmenting Guideline-based CDSS with Experts’ Knowledge.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 370-376

@conference{healthinf17,
author={Nekane Larburu and Naiara Muro and Iván Macía and Eider Sánchez and Hui Wang and John Winder and Jacques Boaud and Brigitte Séroussi},
title={Augmenting Guideline-based CDSS with Experts’ Knowledge},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={370-376},
doi={},
isbn={978-989-758-213-4},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Augmenting Guideline-based CDSS with Experts’ Knowledge
SN - 978-989-758-213-4
AU - Larburu N.
AU - Muro N.
AU - Macía I.
AU - Sánchez E.
AU - Wang H.
AU - Winder J.
AU - Boaud J.
AU - Séroussi B.
PY - 2017
SP - 370
EP - 376
DO -

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