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Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system
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| Zeitschriftentitel: | Health Informatics Journal |
|---|---|
| Personen und Körperschaften: | , , , , , , , |
| In: | Health Informatics Journal, 25, 2019, 4, S. 1232-1243 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
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| Schlagwörter: |
| author_facet |
Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis |
|---|---|
| author |
Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis |
| spellingShingle |
Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis Health Informatics Journal Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system Health Informatics |
| author_sort |
pandey, abhishek |
| spelling |
Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis 1460-4582 1741-2811 SAGE Publications Health Informatics http://dx.doi.org/10.1177/1460458217749883 <jats:p> 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. </jats:p> Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system Health Informatics Journal |
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| title |
Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_unstemmed |
Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
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Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
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Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_full_unstemmed |
Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_short |
Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_sort |
adverse event extraction from structured product labels using the event-based text-mining of health electronic records (ether) system |
| topic |
Health Informatics |
| url |
http://dx.doi.org/10.1177/1460458217749883 |
| publishDate |
2019 |
| physical |
1232-1243 |
| description |
<jats:p> 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. </jats:p> |
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| author | Pandey, Abhishek, Kreimeyer, Kory, Foster, Matthew, Dang, Oanh, Ly, Thomas, Wang, Wei, Forshee, Richard, Botsis, Taxiarchis |
| author_facet | Pandey, Abhishek, Kreimeyer, Kory, Foster, Matthew, Dang, Oanh, Ly, Thomas, Wang, Wei, Forshee, Richard, Botsis, Taxiarchis, Pandey, Abhishek, Kreimeyer, Kory, Foster, Matthew, Dang, Oanh, Ly, Thomas, Wang, Wei, Forshee, Richard, Botsis, Taxiarchis |
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| description | <jats:p> 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. </jats:p> |
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| spelling | Pandey, Abhishek Kreimeyer, Kory Foster, Matthew Dang, Oanh Ly, Thomas Wang, Wei Forshee, Richard Botsis, Taxiarchis 1460-4582 1741-2811 SAGE Publications Health Informatics http://dx.doi.org/10.1177/1460458217749883 <jats:p> 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. </jats:p> Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system Health Informatics Journal |
| spellingShingle | Pandey, Abhishek, Kreimeyer, Kory, Foster, Matthew, Dang, Oanh, Ly, Thomas, Wang, Wei, Forshee, Richard, Botsis, Taxiarchis, Health Informatics Journal, Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system, Health Informatics |
| title | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_full | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_fullStr | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_full_unstemmed | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_short | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| title_sort | adverse event extraction from structured product labels using the event-based text-mining of health electronic records (ether) system |
| title_unstemmed | Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system |
| topic | Health Informatics |
| url | http://dx.doi.org/10.1177/1460458217749883 |