Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system. (December 2019)

• Record Type: Journal Article
• Authors: Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard; Botsis, Taxiarchis
• Is Part Of: Health informatics journal. Volume 25:Number 4(2019)
• Page Start: 1232
• Page End: 1243
• Publication Date: 2019-12
• Subjects: medical dictionary for regulatory activities -- natural language processing -- Structured Product Labels; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/1460458217749883
• Languages: English
• ISSNs: 1460-4582

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.