Name
Abstract | ||
|---|---|---|
Much medical information resides within unstructured free-text documents. Though it is convenient to create documents in free text, it is clear that for certain tasks (e.g., analyzing clinical models), information is most useful when structured within the framework of a data model. We present an information extraction system for clinical documents which is capable of extracting particular values of interest which are embedded within free text. Extraction is based on approximate semantic pattern matching. |
Year | Venue | Keywords |
|---|---|---|
2004 | METMBS '04: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MATHEMATICS AND ENGINEERING TECHNIQUES IN MEDICINE AND BIOLOGICAL SCIENCES | information extraction,natural language processing,electronic medical record,approximate sequence matching |
Field | DocType | Citations |
Data modeling,Information retrieval,Computer science,Information extraction | Conference | 2 |
PageRank | References | Authors |
0.46 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Meng | 1 | 39 | 11.05 |
| Andrew A. Chen | 2 | 6 | 1.70 |
| Roderick Y. Son | 3 | 4 | 1.99 |
| Ricky K. Taira | 4 | 459 | 240.06 |
| Bernard M. Churchill | 5 | 6 | 1.64 |
| Hooshang Kangarloo | 6 | 104 | 17.48 |