Name
Abstract | ||
|---|---|---|
Large amounts of information are locked up in free text components of clinical reports. Surveillance systems that monitor adverse events following immunizations (AEFI) can utilize these components after concept extraction using natural language processing (NLP). Specifically, our method for the identification and filtering of negated concepts using the Unified Medical Language System (UMLS) potentially improves the quality of AEFI surveillance systems. |
Year | Venue | Keywords |
|---|---|---|
2006 | AMIA | natural language processing,unified medical language system |
Field | DocType | Citations |
Information retrieval,Negation,Computer science,Filter (signal processing),Artificial intelligence,Natural language processing,Concept extraction,Unified Medical Language System | Conference | 3 |
PageRank | References | Authors |
0.63 | 1 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Herman D. Tolentino | 1 | 22 | 8.07 |
| Michael D. Matters | 2 | 13 | 1.27 |
| Wikke Walop | 3 | 13 | 1.27 |
| Barbara Law | 4 | 13 | 1.27 |
| Wesley Tong | 5 | 13 | 1.27 |
| Fang Liu | 6 | 22 | 3.48 |
| Paul Fontelo | 7 | 105 | 13.37 |
| Katrin Kohl | 8 | 15 | 1.72 |
| Daniel C. Payne | 9 | 5 | 1.08 |