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Combining Semantic Web Technologies with Evolving Fuzzy Classifier eClass for EHR-Based Phenotyping: A Feasibility Study

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Research and Development in Intelligent Systems XXXI (SGAI 2014)

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 1,956 clinical statements from EHRs. The evaluation performed with clinicians demonstrates the feasibility and practical acceptability of the approach proposed.

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Arguello, M., Lekkas, S., Des, J., Fernandez-Prieto, M., Mikhailov, L. (2014). Combining Semantic Web Technologies with Evolving Fuzzy Classifier eClass for EHR-Based Phenotyping: A Feasibility Study. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXI. SGAI 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-12069-0_15

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  • DOI: https://doi.org/10.1007/978-3-319-12069-0_15

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