CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project
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...
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| Vydané v: | Pharmacoepidemiology and drug safety Ročník 26; číslo 8; s. 998 - 1005 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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England
Wiley Subscription Services, Inc
01.08.2017
John Wiley and Sons Inc |
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| ISSN: | 1053-8569, 1099-1557, 1099-1557 |
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| Abstract | 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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| AbstractList | 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.BACKGROUNDAssessment 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.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.METHODSCodeMapper 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.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.RESULTSAutomated 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.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.CONCLUSIONSAutomatic 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. 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. 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. 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. 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. 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. 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. |
| Author | Weibel, Daniel Mulligen, Erik M. Sturkenboom, Miriam C.J.M. Becker, Benedikt F.H. Romio, Silvana Kors, Jan A. Avillach, Paul |
| AuthorAffiliation | 1 Department of Medical Informatics Erasmus University Medical Center Rotterdam The Netherlands 4 Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands 2 Department of Biomedical Informatics Harvard Medical School Boston MA USA 3 Department of Statistics and Quantitative Methods University of Milano–Bicocca Milan Italy |
| AuthorAffiliation_xml | – name: 2 Department of Biomedical Informatics Harvard Medical School Boston MA USA – name: 1 Department of Medical Informatics Erasmus University Medical Center Rotterdam The Netherlands – name: 4 Department of Epidemiology Erasmus University Medical Center Rotterdam The Netherlands – name: 3 Department of Statistics and Quantitative Methods University of Milano–Bicocca Milan Italy |
| Author_xml | – sequence: 1 givenname: Benedikt F.H. orcidid: 0000-0002-0819-8344 surname: Becker fullname: Becker, Benedikt F.H. email: b.becker@erasmusmc.nl organization: Erasmus University Medical Center – sequence: 2 givenname: Paul surname: Avillach fullname: Avillach, Paul organization: Harvard Medical School – sequence: 3 givenname: Silvana surname: Romio fullname: Romio, Silvana organization: University of Milano–Bicocca – sequence: 4 givenname: Erik M. surname: Mulligen fullname: Mulligen, Erik M. organization: Erasmus University Medical Center – sequence: 5 givenname: Daniel surname: Weibel fullname: Weibel, Daniel organization: Erasmus University Medical Center – sequence: 6 givenname: Miriam C.J.M. surname: Sturkenboom fullname: Sturkenboom, Miriam C.J.M. organization: Erasmus University Medical Center – sequence: 7 givenname: Jan A. surname: Kors fullname: Kors, Jan A. organization: Erasmus University Medical Center |
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| Cites_doi | 10.1016/j.jbi.2012.05.002 10.1055/s-0038-1634585 10.1016/j.jbi.2003.11.001 10.1111/joim.12159 10.1136/amiajnl-2011-000376 10.1002/pds.3439 10.1002/pds.2053 10.1055/s-0038-1634945 10.1136/jamia.1998.0050076 10.1002/pds.2336 10.1016/j.vaccine.2013.04.088 10.2165/00002018-199920020-00002 10.14236/jhi.v13i1.580 10.1542/peds.99.6.765 10.1016/j.jbi.2009.05.002 10.1136/amiajnl-2012-000933 |
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Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts... Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the... Background Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts... |
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| SubjectTerms | concept identification database extraction Databases, Factual - statistics & numerical data Europe - epidemiology Humans International Classification of Diseases - statistics & numerical data Mapping Medical Records Systems, Computerized - statistics & numerical data multiple medical vocabularies Original Report Original Reports Pharmacology semantic operations UMLS Unified Medical Language System - statistics & numerical data Vocabularies & taxonomies |
| Title | CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project |
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