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Conceptual Modeling-Based Cardiopathies Data Management

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Advances in Conceptual Modeling (ER 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13650))

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Abstract

Familiar cardiopathies are a group of heterogeneous disorders that affect the heart. Although their origin has a strong genetic component, most patients’ variations have not been reported and are not classified. Thus, cardiologists struggle to assess whether the origin of the disease is genetic and are unable to provide an appropriate early diagnosis. Providing an enriched context for understanding the role of such variations in these disorders is crucial. This work reports the identification of the conceptual dimensions that are required to provide such context. Then, we instantiate the identified conceptual dimensions in a use case. Finally, the instantiated dimensions are integrated to provide semantic interoperability.

Supported by ACIF/2021/117, INBIO2021/AP2021-05, MICIN/AEI/10.13039/501100011033, and INNEST/2021/57 grants.

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Notes

  1. 1.

    Through grants INBIO 2021/AP2021-05 and INNEST/2021/57.

  2. 2.

    Ions trigger some processes of the heart’s muscle contraction while ATP and ADP are used by cells to obtain energy.

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Authors and Affiliations

  1. PROS Group, Valencian Research Institute (VRAIN), Universitat Politècnica de València, Camí de Vera, s/n, 46022, València, Spain

    Mireia Costa, Alberto García S. & Oscar Pastor

Authors
  1. Mireia Costa
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  2. Alberto García S.
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  3. Oscar Pastor
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Corresponding author

Correspondence to Mireia Costa .

Editor information

Editors and Affiliations

  1. University of Twente, Enschede, The Netherlands

    Renata Guizzardi

  2. Johannes Kepler University Linz, Linz, Austria

    Bernd Neumayr

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Costa, M., García S., A., Pastor, O. (2022). Conceptual Modeling-Based Cardiopathies Data Management. In: Guizzardi, R., Neumayr, B. (eds) Advances in Conceptual Modeling. ER 2022. Lecture Notes in Computer Science, vol 13650. Springer, Cham. https://doi.org/10.1007/978-3-031-22036-4_2

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  • DOI: https://doi.org/10.1007/978-3-031-22036-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22035-7

  • Online ISBN: 978-3-031-22036-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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