Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES.

Journal: Journal of biomedical semantics
Published Date: Sep 18, 2019

Abstract

BACKGROUND: Information in Electronic Health Records is largely stored as unstructured free text. Natural language processing (NLP), or Medical Language Processing (MLP) in medicine, aims at extracting structured information from free text, and is less expensive and time-consuming than manual extraction. However, most algorithms in MLP are institution-specific or address only one clinical need, and thus cannot be broadly applied. In addition, most MLP systems do not detect concepts in misspelled text and cannot detect attribute relationships between concepts. The objective of this study was to develop and evaluate an MLP application that includes generic algorithms for the detection of (misspelled) concepts and of attribute relationships between them.

Authors

  • Martijn G Kersloot
    Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands. m.g.kersloot@amsterdamumc.nl.
  • Francis Lau
    School of Health Information Science, University of Victoria, Victoria, Canada.
  • Ameen Abu-Hanna
    Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.
  • Derk L Arts
    Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.
  • Ronald Cornet
    Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands; Department of Biomedical Engineering, Linköping University, SE-581 83 Linköping, Sweden.

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