Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system

Abhishek Pandey, Kory Kreimeyer, Matthew Foster, Oanh Dang, Thomas Ly, Wei Wang, Richard Forshee, Taxiarchis Botsis

Research output: Contribution to journalArticle

Abstract

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

Original languageEnglish (US)
Pages (from-to)1232-1243
Number of pages12
JournalHealth informatics journal
Volume25
Issue number4
DOIs
StatePublished - Dec 1 2019
Externally publishedYes

    Fingerprint

Keywords

  • medical dictionary for regulatory activities
  • natural language processing
  • Structured Product Labels

ASJC Scopus subject areas

  • Health Informatics

Cite this