@inbook{fd6fc04a6c44499fa0a62ec0fe44c350,
  title     = "Towards a Conceptual Model for Enhancing Reasoning about Clinical Guidelines: A case-study on Comorbidity",
  abstract  = "Computer-Interpretable Guidelines (CIGs) are representations of Clinical Guidelines (CGs) in computer interpretable languages. CIGs have been pointed as an alternative to deal with the various limitations of paper based CGs to support healthcare activities. Although the improvements offered by existing CIG languages, the complexity of the medical domain requires advanced features in order to reuse, share, update, combine or personalize their contents. We propose a conceptual model for representing the content of CGs as a result from an iterative approach that take into account the content of real CGs, CIGs languages and foundational ontologies in order to enhance the reasoning capabilities required to address CIG use-cases. In particular, we apply our approach to the comorbidity use-case and illustrate the model with a realistic case study (Duodenal Ulcer and Transient Ischemic Attack) and compare the results against an existing approach.",
  author    = "{Carretta Zamborlini}, Veruska and {Da Silveira}, Marcos and Cedric Pruski and {ten Teije}, Annette and {van Harmelen}, Frank",
  note      = "Proceedings title: Proceedings of 6th International Workshop Knowledge Representation for Health-Care (KR4HC). Publisher: Springer Editors: S. Miksch, D. Riano, A. ten Teije",
  year      = "2014",
  doi       = "10.1007/978-3-319-13281-5_3",
  volume    = "8903",
  series    = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  publisher = "Springer/Verlag",
  pages     = "29--44",
  booktitle = "Knowledge Representation for Health Care - 6th International Workshop, KR4HC 2014, Revised Selected Papers",
}

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CIGs have been pointed as an alternative to deal with the various limitations of paper based CGs to support healthcare activities. Although the improvements offered by existing CIG languages, the complexity of the medical domain requires advanced features in order to reuse, share, update, combine or personalize their contents. We propose a conceptual model for representing the content of CGs as a result from an iterative approach that take into account the content of real CGs, CIGs languages and foundational ontologies in order to enhance the reasoning capabilities required to address CIG use-cases. 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