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Ordered Time-Independent CIG Learning

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Biological and Medical Data Analysis (ISBMDA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3337))

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Abstract

Clinical practice guidelines are medical and surgical statements to assist practitioners in the therapy procedure. Recently, the concept computer-interpretable guideline (CIG) has been introduced to describe formal descriptions of clinical practice guidelines. Ordered time-independent one-visit CIGs are a sort of CIG wich are able to cope with the description and use of real therapies. Here, this representation model and a machine learning algorithm to construct such CIGs from the hospital databases or from predefined CIGs are introduced and tested within the domain of attrial fibrillation.

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Author information

Authors and Affiliations

  1. Department of Computer Sciences and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

Authors
  1. David Riaño
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Editor information

Editors and Affiliations

  1. Biomedical Informatics Group, Artificial Intelligence Lab, Polytechnical University of Madrid, Spain

    José María Barreiro

  2. Medical Bioinformatics Department, Institute of Health ‘Carlos III’, Ctra. Majadahonda-Pozuelo, km 2. 28220 Majadahonda, Madrid,  

    Fernando Martín-Sánchez

  3. Biomedical Informatics Group, Dep. Inteligencia Artificial, Facultad de Informática, Universidad Politécnica de Madrid, Spain

    Víctor Maojo

  4. GRIB, IMIM/UPF Barcelona, Spain

    Ferran Sanz

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© 2004 Springer-Verlag Berlin Heidelberg

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Riaño, D. (2004). Ordered Time-Independent CIG Learning. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds) Biological and Medical Data Analysis. ISBMDA 2004. Lecture Notes in Computer Science, vol 3337. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30547-7_13

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  • DOI: https://doi.org/10.1007/978-3-540-30547-7_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23964-2

  • Online ISBN: 978-3-540-30547-7

  • eBook Packages: Springer Book Archive

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