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Personalization of Ontologies Visualization: Use Case of Diabetes

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Current Trends in Semantic Web Technologies: Theory and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 815))

Abstract

In P4 medicine, which faces the challenge of building personalized medicine, a semantic-based personalized visualization is key to enhance both patient and other stakeholders’ experience. Ontologies are a formal way to represent knowledge, and their visualization depends considerably on the role or user who is visualizing them. In the same way, like databases have virtual tables or views to tailor the data to application needs, ontologies should facilitate different perspectives on semantic data, customized to the needs of all stakeholders. This is especially true in the case of medicine, where the data consumers have quite varied roles, like patient, professional or policymaker. This study presents the current state of the art of personalization in ontology visualization initiatives, a brief summary of the diabetes mellitus domain, and existing ontologies in the diabetes domain. It also presents an approach for the personalization of ontologies visualization based on the implementation of the overview, zoom/filter and details interaction patterns. This is done by adapting the Rhizomer tool so different views can be generated in the context of personalized medicine. All this is validated through a use-case of a new ontology to model the diabetes domain from an existing open dataset of around 70,000 diabetic patients extracted from American hospitals. The conclusion is that the application of this approach has the potential to enhance personalization of medicine ontologies and their visualization.

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Abbreviations

ATC:

Anatomical Therapeutic Chemical Classification System

BFO:

Basic Formal Ontology

CBR:

Case-based reasoning

CSS:

Cascading Style Sheets

DDO:

Diabetes Mellitus Diagnosis Ontology

DMTO:

Diabetes Mellitus treatment ontology

EHR:

Electronic Health Records

HbA1c:

Haemoglobin A1c or glicated haemoglobin

HTML:

Hypertext Markup Language

ICD:

International Classification of Diseases

ICF:

International Classification of Functioning, Disability and Health

OGMS:

Ontology for General Medical Science

RDF:

Resource Description Framework

SNOMED CT:

Systematized Nomenclature of Medicine – Clinical Terms

SPARQL:

SPARQL Protocol and RDF Query Language

VOWL:

Visual Notation for OWL ontologies

WHO:

World Health Organization

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Acknowledgements

This research has been partially funded by the Catalonia Competitiveness Agency (ACC1Ó).

Author information

Authors and Affiliations

  1. Eurecat, Centre Tecnològic de Catalunya, Unitat de eHealth, C/Bilbao 72, 08005, Barcelona, Spain

    Laia Subirats

  2. Universitat Oberta de Catalunya, eHealth Center, Rambla del Poblenou 156, 08018, Barcelona, Spain

    Laia Subirats

  3. Universitat de Lleida, Jaume II 69, 25001, Lleida, Spain

    Rosa Gil & Roberto García

Authors
  1. Laia Subirats
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  2. Rosa Gil
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  3. Roberto García
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Contributions

R. García., R. Gil and L. Subirats conceived the use case; R. García and R. Gil developed Rhizomik, all authors contributed to the analysis and wrote the paper.

Corresponding author

Correspondence to Roberto García .

Editor information

Editors and Affiliations

  1. Division of Research and Postgraduate Studies, Tecnológico Nacional de México/I.T. Orizaba, Orizaba, Mexico

    Giner Alor-Hernández

  2. Division of Research and Postgraduate Studies, CONACYT-Instituto Tecnológico de Orizaba, Orizaba, Mexico

    José Luis Sánchez-Cervantes

  3. Centro de Tecnología Biomédica (CTB), Universidad Politécnica de Madrid, Pozuelo de Alarcon, Madrid, Spain

    Alejandro Rodríguez-González

  4. Facultad de Informática, Universidad de Murcia, Murcia, Spain

    Rafael Valencia-García

Ethics declarations

Ethics approval and consent to participate

Open data is used and it is extracted from UCI machine learning repository. The citation requested has been done.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Data sharing and code is available under request and the visualization is openly available at http://rhizomik.net/diabetes.

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Subirats, L., Gil, R., García, R. (2019). Personalization of Ontologies Visualization: Use Case of Diabetes. In: Alor-Hernández, G., Sánchez-Cervantes, J., Rodríguez-González, A., Valencia-García, R. (eds) Current Trends in Semantic Web Technologies: Theory and Practice. Studies in Computational Intelligence, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-030-06149-4_1

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