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 language | English (US) |
---|---|
Pages (from-to) | 1232-1243 |
Number of pages | 12 |
Journal | Health informatics journal |
Volume | 25 |
Issue number | 4 |
DOIs | |
State | Published - Dec 1 2019 |
Externally published | Yes |
Keywords
- Structured Product Labels
- medical dictionary for regulatory activities
- natural language processing
ASJC Scopus subject areas
- Health Informatics