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Ibero-American Conference on Artificial Intelligence

IBERAMIA 2016: Advances in Artificial Intelligence - IBERAMIA 2016 pp 77–88Cite as

A Constraint-Based Approach for the Conciliation of Clinical Guidelines

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10022))

Abstract

The medical domain often arises new challenges to Artificial Intelligence. An emerging challenge is the support for the treatment of patients affected by multiple pathologies (comorbid patients). In the medical context, clinical practice guidelines (CPGs) are usually adopted to provide physicians with evidence-based recommendations, considering only single pathologies. To support physicians in the treatment of comorbid patients, suitable methodologies must be devised to “merge” CPGs. Techniques like replanning or scheduling, traditionally adopted in AI to “merge” plans, must be extended and adapted to fit the requirements of the medical domain. In this paper, we propose a novel methodology, that we term “conciliation”, to merge multiple CPGs, supporting the treatments of comorbid patients.

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Notes

  1. 1.

    Actually, there are some domain-specific cases in which we consider a CSP “non-consistent” also in case all the domains are not empty. See, for instance, the example of Subsect. 3.4.

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Authors and Affiliations

  1. DISIT, Institute of Computer Science, Università del Piemonte Orientale, Alessandria, Italy

    Luca Piovesan & Paolo Terenziani

Authors
  1. Luca Piovesan
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  2. Paolo Terenziani
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Correspondence to Luca Piovesan .

Editor information

Editors and Affiliations

  1. INAOE , Tonantzintla, Mexico

    Manuel Montes y Gómez

  2. Astrofisica Optica y Electronica, INAOE , Puebla, Mexico

    Hugo Jair Escalante

  3. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Alberto Segura

  4. Universidad Nacional de Costa Rica , Heredia, Costa Rica

    Juan de Dios Murillo

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Piovesan, L., Terenziani, P. (2016). A Constraint-Based Approach for the Conciliation of Clinical Guidelines. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-47955-2_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47954-5

  • Online ISBN: 978-3-319-47955-2

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