Name
Abstract | ||
|---|---|---|
With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. We aim to extract adverse drug events and effects from records. As the first step of this challenge, this study assessed (1) how much adverse-effect information is contained in records, and (2) automatic extracting accuracy of the current standard Natural Language Processing (NLP) system. Results revealed that 7.7% of records include adverse event information, and that 59% of them (4.5% in total) can be extracted automatically. This result is particularly encouraging, considering the massive amounts of records, which are increasing daily. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/978-1-60750-588-4-739 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Adverse effect,Side effect,Drug trial,Natural language processing (NLP) | Data mining,Adverse drug effects,Adverse effect,Information extraction,Medicine | Conference |
Volume | Issue | ISSN |
160 | Pt 1 | 0926-9630 |
Citations | PageRank | References |
24 | 1.27 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eiji Aramaki | 1 | 371 | 45.89 |
| Yasuhide Miura | 2 | 44 | 3.23 |
| Masatsugu Tonoike | 3 | 24 | 1.27 |
| Tomoko Ohkuma | 4 | 97 | 15.49 |
| Hiroshi Masuichi | 5 | 24 | 1.27 |
| Kayo Waki | 6 | 24 | 1.61 |
| Kazuhiko Ohe | 7 | 115 | 15.91 |