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A deep database of medical abbreviations and acronyms for natural language processing
The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-In...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172575/ https://www.ncbi.nlm.nih.gov/pubmed/34078918 http://dx.doi.org/10.1038/s41597-021-00929-4 |
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author | Grossman Liu, Lisa Grossman, Raymond H. Mitchell, Elliot G. Weng, Chunhua Natarajan, Karthik Hripcsak, George Vawdrey, David K. |
author_facet | Grossman Liu, Lisa Grossman, Raymond H. Mitchell, Elliot G. Weng, Chunhua Natarajan, Karthik Hripcsak, George Vawdrey, David K. |
author_sort | Grossman Liu, Lisa |
collection | PubMed |
description | The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations. |
format | Online Article Text |
id | pubmed-8172575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81725752021-06-07 A deep database of medical abbreviations and acronyms for natural language processing Grossman Liu, Lisa Grossman, Raymond H. Mitchell, Elliot G. Weng, Chunhua Natarajan, Karthik Hripcsak, George Vawdrey, David K. Sci Data Data Descriptor The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations. Nature Publishing Group UK 2021-06-02 /pmc/articles/PMC8172575/ /pubmed/34078918 http://dx.doi.org/10.1038/s41597-021-00929-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Grossman Liu, Lisa Grossman, Raymond H. Mitchell, Elliot G. Weng, Chunhua Natarajan, Karthik Hripcsak, George Vawdrey, David K. A deep database of medical abbreviations and acronyms for natural language processing |
title | A deep database of medical abbreviations and acronyms for natural language processing |
title_full | A deep database of medical abbreviations and acronyms for natural language processing |
title_fullStr | A deep database of medical abbreviations and acronyms for natural language processing |
title_full_unstemmed | A deep database of medical abbreviations and acronyms for natural language processing |
title_short | A deep database of medical abbreviations and acronyms for natural language processing |
title_sort | deep database of medical abbreviations and acronyms for natural language processing |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8172575/ https://www.ncbi.nlm.nih.gov/pubmed/34078918 http://dx.doi.org/10.1038/s41597-021-00929-4 |
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