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Personalization of Ontologies Visualization: Use Case of Diabetes

  • Laia Subirats
  • Rosa Gil
  • Roberto GarcíaEmail author
Chapter
Part of the Studies in Computational Intelligence book series (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.

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

Notes

Acknowledgements

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

Author’s 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.

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Eurecat, Centre Tecnològic de CatalunyaUnitat de eHealthBarcelonaSpain
  2. 2.Universitat Oberta de Catalunya, eHealth CenterBarcelonaSpain
  3. 3.Universitat de LleidaLleidaSpain

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