Name
Abstract | ||
|---|---|---|
Extracting key concepts from clinical texts for indexing is an important task in implementing a medical digital library. Several methods are proposed for mapping free text into standard terms defined by the Unified Medical Language System (UMLS). For example, natural language processing techniques are used to map identified noun phrases into concepts. They are, however, not appropriate for real time applications. Therefore, in this paper, we present a new algorithm for generating all valid UMLS concepts by permuting the set of words in the input text and then filtering out the irrelevant concepts via syntactic and semantic filtering. We have implemented the algorithm as a web-based service that provides a search interface for researchers and computer programs. Our preliminary experiment shows that the algorithm is effective at discovering relevant UMLS concepts while achieving a throughput of 43K bytes of text per second. The tool can extract key concepts from clinical texts for indexing. |
Year | Venue | Keywords |
|---|---|---|
2003 | AMIA | internet,unified medical language system,semantics,natural language processing,algorithms,digital library,indexation,noun phrase |
Field | DocType | ISSN |
Noun phrase,Byte,Information retrieval,Computer science,Search engine indexing,Natural language processing,Artificial intelligence,Digital library,Unified Medical Language System,Syntax,Semantics,The Internet | Conference | 1942-597X |
Citations | PageRank | References |
34 | 1.96 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghua Zou | 1 | 133 | 11.09 |
| Wesley W. Chu | 2 | 2311 | 789.42 |
| Craig Morioka | 3 | 34 | 1.96 |
| Gregory H. Leazer | 4 | 137 | 15.72 |
| Hooshang Kangarloo | 5 | 104 | 17.48 |