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Integrating Medical Scientific Knowledge with the Semantically Quantified Self

  • Allan Third
  • George Gkotsis
  • Eleni Kaldoudi
  • George Drosatos
  • Nick Portokallidis
  • Stefanos Roumeliotis
  • Kalliopi Pafili
  • John Domingue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9981)

Abstract

The assessment of risk in medicine is a crucial task, and depends on scientific knowledge derived by systematic clinical studies on factors affecting health, as well as on particular knowledge about the current status of a particular patient. Existing non-semantic risk prediction tools are typically based on hardcoded scientific knowledge, and only cover a very limited range of patient states. This makes them rapidly out of date, and limited in application, particularly for patients with multiple co-occurring conditions. In this work we propose an integration of Semantic Web and Quantified Self technologies to create a framework for calculating clinical risk predictions for patients based on self-gathered biometric data. This framework relies on generic, reusable ontologies for representing clinical risk, and sensor readings, and reasoning to support the integration of data represented according to these ontologies. The implemented framework shows a wide range of advantages over existing risk calculation.

Keywords

Health Comorbidities Risk factor Scientific modelling Knowledge capture Semantics Ontology Linked data 

Notes

Acknowledgments

This work was supported by the FP7-ICT project CARRE (Grant No. 611140), funded in part by the European Commission. We express our gratitude to all project team members for fruitful discussions.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Allan Third
    • 1
  • George Gkotsis
    • 2
  • Eleni Kaldoudi
    • 3
  • George Drosatos
    • 3
  • Nick Portokallidis
    • 3
  • Stefanos Roumeliotis
    • 3
  • Kalliopi Pafili
    • 3
  • John Domingue
    • 1
  1. 1.Knowledge Media InstituteOpen UniversityMilton KeynesUK
  2. 2.King’s College London, Biomedical Research Centre NucleusLondonUK
  3. 3.School of MedicineDemocritus University of ThraceAlexandroupoliGreece

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