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A dataset of 200 structured product labels annotated for adverse drug reactions
Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789866/ https://www.ncbi.nlm.nih.gov/pubmed/29381145 http://dx.doi.org/10.1038/sdata.2018.1 |
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author | Demner-Fushman, Dina Shooshan, Sonya E. Rodriguez, Laritza Aronson, Alan R. Lang, Francois Rogers, Willie Roberts, Kirk Tonning, Joseph |
author_facet | Demner-Fushman, Dina Shooshan, Sonya E. Rodriguez, Laritza Aronson, Alan R. Lang, Francois Rogers, Willie Roberts, Kirk Tonning, Joseph |
author_sort | Demner-Fushman, Dina |
collection | PubMed |
description | Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars. |
format | Online Article Text |
id | pubmed-5789866 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-57898662018-02-07 A dataset of 200 structured product labels annotated for adverse drug reactions Demner-Fushman, Dina Shooshan, Sonya E. Rodriguez, Laritza Aronson, Alan R. Lang, Francois Rogers, Willie Roberts, Kirk Tonning, Joseph Sci Data Data Descriptor Adverse drug reactions (ADRs), unintended and sometimes dangerous effects that a drug may have, are one of the leading causes of morbidity and mortality during medical care. To date, there is no structured machine-readable authoritative source of known ADRs. The United States Food and Drug Administration (FDA) partnered with the National Library of Medicine to create a pilot dataset containing standardised information about known adverse reactions for 200 FDA-approved drugs. The Structured Product Labels (SPLs), the documents FDA uses to exchange information about drugs and other products, were manually annotated for adverse reactions at the mention level to facilitate development and evaluation of text mining tools for extraction of ADRs from all SPLs. The ADRs were then normalised to the Unified Medical Language System (UMLS) and to the Medical Dictionary for Regulatory Activities (MedDRA). We present the curation process and the structure of the publicly available database SPL-ADR-200db containing 5,098 distinct ADRs. The database is available at https://bionlp.nlm.nih.gov/tac2017adversereactions/; the code for preparing and validating the data is available at https://github.com/lhncbc/fda-ars. Nature Publishing Group 2018-01-30 /pmc/articles/PMC5789866/ /pubmed/29381145 http://dx.doi.org/10.1038/sdata.2018.1 Text en Copyright © 2018, The Author(s) http://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/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article. |
spellingShingle | Data Descriptor Demner-Fushman, Dina Shooshan, Sonya E. Rodriguez, Laritza Aronson, Alan R. Lang, Francois Rogers, Willie Roberts, Kirk Tonning, Joseph A dataset of 200 structured product labels annotated for adverse drug reactions |
title | A dataset of 200 structured product labels annotated for adverse drug reactions |
title_full | A dataset of 200 structured product labels annotated for adverse drug reactions |
title_fullStr | A dataset of 200 structured product labels annotated for adverse drug reactions |
title_full_unstemmed | A dataset of 200 structured product labels annotated for adverse drug reactions |
title_short | A dataset of 200 structured product labels annotated for adverse drug reactions |
title_sort | dataset of 200 structured product labels annotated for adverse drug reactions |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5789866/ https://www.ncbi.nlm.nih.gov/pubmed/29381145 http://dx.doi.org/10.1038/sdata.2018.1 |
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