Name
Abstract | ||
|---|---|---|
In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1186/s13326-015-0033-1 | Journal of Biomedical Semantics |
Keywords | Field | DocType |
Medial Collateral Ligament, Lateral Meniscus, Medial Meniscus, Unify Medical Language System, Name Entity Recognition | Data science,Data mining,Ontology,Computer science,Lateral meniscus,Information extraction,Knee Joint,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
6 | 1 | 2041-1480 |
Citations | PageRank | References |
11 | 0.67 | 27 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irena Spasić | 1 | 354 | 32.55 |
| Bo Zhao | 2 | 11 | 1.01 |
| Christopher B. Jones | 3 | 1067 | 95.29 |
| Kate Button | 4 | 19 | 1.81 |