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Automatic Identification of Individual Drugs in Death Certificates.
Kho, Soon Jye; Sheth, Amit; Bodenreider, Olivier.
Affiliation
  • Kho SJ; Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, USA.
  • Sheth A; Kno.e.sis Center, Department of Computer Science and Engineering, Wright State University, Dayton, Ohio, USA.
  • Bodenreider O; National Library of Medicine, Bethesda, Maryland, USA.
Stud Health Technol Inform ; 264: 183-187, 2019 Aug 21.
Article in En | MEDLINE | ID: mdl-31437910
ABSTRACT

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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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Death Certificates / Drug Overdose / RxNorm Type of study: Diagnostic_studies Limits: Humans Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2019 Document type: Article Affiliation country:

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Death Certificates / Drug Overdose / RxNorm Type of study: Diagnostic_studies Limits: Humans Language: En Journal: Stud Health Technol Inform Journal subject: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Year: 2019 Document type: Article Affiliation country:
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