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; Mikhailov, L

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.

• Publication Date: Dec 1, 2014
• Deposit Date: Jun 17, 2015
• Publicly Available Date: Aug 13, 2018
• Pages: 195-208
• Series Title: Research and Development in Intelligent Systems
• Series Number: XXXI
• Book Title: Research and Development in Intelligent Systems XXXI : Incorporating Applications and Innovations in Intelligent Systems XXII
• ISBN: 9783319120690
• DOI: https://doi.org/10.1007/978-3-319-12069-0_15
• Publisher URL: http://dx.doi.org/10.1007/978-3-319-12069-0_15
• Related Public URLs: http://dx.doi.org/10.1007/978-3-319-12069-0
• Additional Information: Additional Information : Best Refereed Paper in Application Stream.