Evaluating MedDRA-to-ICD terminology mappings
Abstract Background In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and Interna...
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-023-02375-1 |
| _version_ | 1797274369631715328 |
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| author | Xinyuan Zhang Yixue Feng Fang Li Jin Ding Danyal Tahseen Ezekiel Hinojosa Yong Chen Cui Tao |
| author_facet | Xinyuan Zhang Yixue Feng Fang Li Jin Ding Danyal Tahseen Ezekiel Hinojosa Yong Chen Cui Tao |
| author_sort | Xinyuan Zhang |
| collection | DOAJ |
| description | Abstract Background In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. Results The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. Conclusion Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS. |
| first_indexed | 2024-03-07T14:57:28Z |
| format | Article |
| id | doaj.art-68d5306c90bf4b5093e8e5c64eebea74 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-03-07T14:57:28Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-68d5306c90bf4b5093e8e5c64eebea742024-03-05T19:20:03ZengBMCBMC Medical Informatics and Decision Making1472-69472024-02-0123S411210.1186/s12911-023-02375-1Evaluating MedDRA-to-ICD terminology mappingsXinyuan Zhang0Yixue Feng1Fang Li2Jin Ding3Danyal Tahseen4Ezekiel Hinojosa5Yong Chen6Cui Tao7McWilliam School of Biomedical Informatics, University of Texas Health Science Center at HoustonSchool of Engineering and Applied Science, University of PennsylvaniaMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at HoustonMcGovern Medical School, University of Texas Health Science Center at HoustonMcGovern Medical School, University of Texas Health Science Center at HoustonThe Perelman School of Medicine, University of PennsylvaniaMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at HoustonAbstract Background In this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage. Results The identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD. Conclusion Some of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.https://doi.org/10.1186/s12911-023-02375-1The medical dictionary for regulatory activities (MedDRA)International classification of diseases (ICD)Unified medical language system (UMLS)Observational medical outcomes partnership common data model (OMOP CDM)Terminology mapping |
| spellingShingle | Xinyuan Zhang Yixue Feng Fang Li Jin Ding Danyal Tahseen Ezekiel Hinojosa Yong Chen Cui Tao Evaluating MedDRA-to-ICD terminology mappings BMC Medical Informatics and Decision Making The medical dictionary for regulatory activities (MedDRA) International classification of diseases (ICD) Unified medical language system (UMLS) Observational medical outcomes partnership common data model (OMOP CDM) Terminology mapping |
| title | Evaluating MedDRA-to-ICD terminology mappings |
| title_full | Evaluating MedDRA-to-ICD terminology mappings |
| title_fullStr | Evaluating MedDRA-to-ICD terminology mappings |
| title_full_unstemmed | Evaluating MedDRA-to-ICD terminology mappings |
| title_short | Evaluating MedDRA-to-ICD terminology mappings |
| title_sort | evaluating meddra to icd terminology mappings |
| topic | The medical dictionary for regulatory activities (MedDRA) International classification of diseases (ICD) Unified medical language system (UMLS) Observational medical outcomes partnership common data model (OMOP CDM) Terminology mapping |
| url | https://doi.org/10.1186/s12911-023-02375-1 |
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