Name
Title | ||
|---|---|---|
Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system. |
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 81percent and 92percent, 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1177/1460458217749883 | HEALTH INFORMATICS JOURNAL |
Keywords | Field | DocType |
medical dictionary for regulatory activities,natural language processing,Structured Product Labels | Text mining,World Wide Web,MedDRA,XML,Knowledge management,Adverse effect,Structured product,Electronic records,Medicine,Markup language,Drug administration | Journal |
Volume | Issue | ISSN |
25.0 | 4.0 | 1460-4582 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abhishek Pandey | 1 | 17 | 2.52 |
| Kory Kreimeyer | 2 | 17 | 2.52 |
| Matthew Foster | 3 | 17 | 2.52 |
| Taxiarchis Botsis | 4 | 99 | 10.86 |
| Oanh Dang | 5 | 1 | 0.69 |
| Thomas Ly | 6 | 0 | 0.34 |
| Wei Wang | 7 | 10 | 7.04 |
| Richard Forshee | 8 | 16 | 2.12 |