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Automatic Identification of Individual Drugs in Death Certificates

BACKGROUND. Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging du...

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Detalles Bibliográficos
Autores principales: Kho, Soon Jye, Sheth, Amit, Bodenreider, Olivier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529587/
https://www.ncbi.nlm.nih.gov/pubmed/31437910
http://dx.doi.org/10.3233/SHTI190208
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author Kho, Soon Jye
Sheth, Amit
Bodenreider, Olivier
author_facet Kho, Soon Jye
Sheth, Amit
Bodenreider, Olivier
author_sort Kho, Soon Jye
collection PubMed
description BACKGROUND. Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging due to the variability of drug names. OBJECTIVES. To automatically identify individual drugs in death certificates. METHODS. We use RxNorm to collect variants for drug names (generic names, synonyms, brand names) and we algorithmically generate common misspellings. We use this automatically compiled list to identify drug mentions from 703,106 death certificates and compare the performance of our automated approach to that of a manually curated list of drug names. RESULTS. Our automated approach shows a slight loss in recall (4.3%) compared to the manual approach (for individual drugs), due in part to acronyms. CONCLUSIONS. Maintenance of a manually curated list of drugs is not sustainable and our approach offers a viable alternative.
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spelling pubmed-85295872021-10-21 Automatic Identification of Individual Drugs in Death Certificates Kho, Soon Jye Sheth, Amit Bodenreider, Olivier Stud Health Technol Inform Article BACKGROUND. Establishing trends of drug overdoses requires the identification of individual drugs in death certificates, not supported by coding with the International Classification of Diseases. However, identifying drug mentions from the literal portion of death certificates remains challenging due to the variability of drug names. OBJECTIVES. To automatically identify individual drugs in death certificates. METHODS. We use RxNorm to collect variants for drug names (generic names, synonyms, brand names) and we algorithmically generate common misspellings. We use this automatically compiled list to identify drug mentions from 703,106 death certificates and compare the performance of our automated approach to that of a manually curated list of drug names. RESULTS. Our automated approach shows a slight loss in recall (4.3%) compared to the manual approach (for individual drugs), due in part to acronyms. CONCLUSIONS. Maintenance of a manually curated list of drugs is not sustainable and our approach offers a viable alternative. 2019-08-21 /pmc/articles/PMC8529587/ /pubmed/31437910 http://dx.doi.org/10.3233/SHTI190208 Text en https://creativecommons.org/licenses/by/4.0/This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
spellingShingle Article
Kho, Soon Jye
Sheth, Amit
Bodenreider, Olivier
Automatic Identification of Individual Drugs in Death Certificates
title Automatic Identification of Individual Drugs in Death Certificates
title_full Automatic Identification of Individual Drugs in Death Certificates
title_fullStr Automatic Identification of Individual Drugs in Death Certificates
title_full_unstemmed Automatic Identification of Individual Drugs in Death Certificates
title_short Automatic Identification of Individual Drugs in Death Certificates
title_sort automatic identification of individual drugs in death certificates
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529587/
https://www.ncbi.nlm.nih.gov/pubmed/31437910
http://dx.doi.org/10.3233/SHTI190208
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