The Open UniversitySkip to content

Integrating Medical Scientific Knowledge with the Semantically Quantified Self

Third, Allan; Gkotsis, George; Kaldoudi, Eleni; Drosatos, George; Portokallidis, Nick; Roumeliotis, Stefanos; Pafili, Kalliopi and Domingue, John (2016). Integrating Medical Scientific Knowledge with the Semantically Quantified Self. In: 15th International Semantic Web Conference, ISWC 2016, Lecture Notes in Computer Science, 9981 pp. 566–580.

Full text available as:
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (524kB) | Preview
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


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 hard-coded 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 This makes them rapidly out of date, and limited in application, particularly for patients with multiple co-occurring conditions.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.

Item Type: Conference or Workshop Item
Copyright Holders: 2016 Springer International Publishing AG
ISSN: 0302-9743
Extra Information: Print ISBN: 978-3-319-46522-7

15th International Semantic Web Conference, Kobe, Japan, October 17–21, 2016, Proceedings, Part I
Editors: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (Eds.)
Keywords: health; comorbidities; risk factor; scientific modelling; knowledge capture; semantics; ontology; linked data
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Related URLs:
Item ID: 52927
Depositing User: Kay Dave
Date Deposited: 22 Jan 2018 10:56
Last Modified: 05 Jun 2020 23:19
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU