Name
Abstract | ||
|---|---|---|
Statistical text mining and natural language processing have been shown to be effective for extracting useful information from medical documents. However, neither technique is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed to extract information from the semi-structured text using machine learning and a rule based annotator. Features for the learning machine were suggested by prior work, and by examining text, and selecting attributes that help distinguish classes of text lines. Classes were derived empirically from text and guided by an ontology developed by the VHA's Consortium for Health Informatics Research (CHIR). Decision trees were evaluated for class predictions on 15,103 lines of text achieved an overall accuracy of 98.5 percent. The class labels applied to the lines were then used for annotating semi-structured text elements. TagLine achieved F-measure over 0.9 for each of the structures, which included tables, slots and fillers. |
Year | Venue | DocType |
|---|---|---|
2014 | AMIA | Conference |
Volume | ISSN | Citations |
2014 | 1942-597X | 1 |
PageRank | References | Authors |
0.35 | 44 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dezon K Finch | 1 | 1 | 0.35 |
| James A McCart | 2 | 1 | 0.35 |
| Stephen L Luther | 3 | 1 | 0.35 |