Skip to the content

Combining semantic web technologies with evolving fuzzy classifier eClass for EHR-based phenotyping : a feasibility study

Arguello, M, Lekkas, S, Des, J, Fernandez Prieto, MJ and Mikhailov, L 2014, 'Combining semantic web technologies with evolving fuzzy classifier eClass for EHR-based phenotyping : a feasibility study' , in: Research and Development in Intelligent Systems XXXI : Incorporating Applications and Innovations in Intelligent Systems XXII , Research and Development in Intelligent Systems, XIV (XXXI) , Springer International Publishing, Switzerland, pp. 195-208.

[img] PDF - Accepted Version
Restricted to Repository staff only

Download (414kB) | Request a copy

Abstract

In parallel to nation-wide efforts for setting up shared electronic health records (EHRs) across healthcare settings, several large-scale national and international projects are developing, validating, and deploying electronic EHR oriented phenotype algorithms that aim at large-scale use of EHRs data for genomic studies. A current bottleneck in using EHRs data for obtaining computable phenotypes is to transform the raw EHR data into clinically relevant features. The research study presented here proposes a novel combination of Semantic Web technologies with the on-line evolving fuzzy classifier eClass to obtain and validate EHR-driven computable phenotypes derived from 1956 clinical statements from EHRs. The evaluation performed with clinicians demonstrates the feasibility and practical acceptability of the approach proposed.

Item Type: Book Section
Editors: Bramer, M and Petridis, M
Additional Information: Best Refereed Paper in Application Stream.
Themes: Health and Wellbeing
Schools: Schools > School of Humanities, Languages & Social Sciences > Centre for Linguistics and Applied Linguistics
Publisher: Springer International Publishing
Refereed: Yes
Series Name: Research and Development in Intelligent Systems
ISBN: 9783319120690
Related URLs:
Funders: Non funded research
Depositing User: MJ Fernandez-Prieto
Date Deposited: 17 Jun 2015 15:27
Last Modified: 17 Jun 2015 15:27
URI: http://usir.salford.ac.uk/id/eprint/35263

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)

Downloads

Downloads per month over past year