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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. Lecture Notes in Computer Science, 9981 pp. 566–580.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-46523-4_34
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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 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 This makes them rapidly out of date, and limited in application, particularly for patients with multiple co-occurring conditions.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: Journal 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)
Item ID: 52927
Depositing User: Kay Dave
Date Deposited: 22 Jan 2018 10:56
Last Modified: 21 Jan 2019 19:09
URI: http://oro.open.ac.uk/id/eprint/52927
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