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CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project.
Becker, Benedikt F H; Avillach, Paul; Romio, Silvana; van Mulligen, Erik M; Weibel, Daniel; Sturkenboom, Miriam C J M; Kors, Jan A.
Afiliación
  • Becker BFH; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Avillach P; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Romio S; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • van Mulligen EM; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Weibel D; Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy.
  • Sturkenboom MCJM; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
  • Kors JA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
Pharmacoepidemiol Drug Saf ; 26(8): 998-1005, 2017 Aug.
Article en En | MEDLINE | ID: mdl-28657162
BACKGROUND: Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the harmonization of codes as different vocabularies are being used across countries. In this paper, we present a web application called CodeMapper, which assists in the mapping of case definitions to codes from different vocabularies, while keeping a transparent record of the complete mapping process. METHODS: CodeMapper builds upon coding vocabularies contained in the Metathesaurus of the Unified Medical Language System. The mapping approach consists of three phases. First, medical concepts are automatically identified in a free-text case definition. Second, the user revises the set of medical concepts by adding or removing concepts, or expanding them to related concepts that are more general or more specific. Finally, the selected concepts are projected to codes from the targeted coding vocabularies. We evaluated the application by comparing codes that were automatically generated from case definitions by applying CodeMapper's concept identification and successive concept expansion, with reference codes that were manually created in a previous epidemiological study. RESULTS: Automated concept identification alone had a sensitivity of 0.246 and positive predictive value (PPV) of 0.420 for reproducing the reference codes. Three successive steps of concept expansion increased sensitivity to 0.953 and PPV to 0.616. CONCLUSIONS: Automatic concept identification in the case definition alone was insufficient to reproduce the reference codes, but CodeMapper's operations for concept expansion provide an effective, efficient, and transparent way for reproducing the reference codes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clasificación Internacional de Enfermedades / Bases de Datos Factuales / Sistemas de Registros Médicos Computarizados / Unified Medical Language System Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Pharmacoepidemiol Drug Saf Asunto de la revista: EPIDEMIOLOGIA / TERAPIA POR MEDICAMENTOS Año: 2017 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Clasificación Internacional de Enfermedades / Bases de Datos Factuales / Sistemas de Registros Médicos Computarizados / Unified Medical Language System Tipo de estudio: Prognostic_studies Límite: Humans País/Región como asunto: Europa Idioma: En Revista: Pharmacoepidemiol Drug Saf Asunto de la revista: EPIDEMIOLOGIA / TERAPIA POR MEDICAMENTOS Año: 2017 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido