Name
Title | ||
|---|---|---|
Linking Medications and Their Attributes in Clinical Notes and Clinical Trial Announcements for Information Extraction: A Sequence Labeling Approach |
Abstract | ||
|---|---|---|
The goal of this work is to evaluate binary classification and sequence labeling methods for medication-attribute linkage detection in two clinical corpora. The results show that with parsimonious feature sets both the Support Vector Machine (SVM)-based binary classification and Conditional Random Field (CRF)-based multi-layered sequence labeling methods are achieving high performance. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/HISB.2012.27 | HISB |
Keywords | Field | DocType |
high performance,binary classification,conditional random field-based multilayered sequence labeling,clinical notes,statistical analysis,parsimonious feature,pattern classification,information retrieval,clinical trial announcements,multi-layered sequence,svm-based binary classification,clinical corpus,medicine,support vector machine,conditional random field,information extraction,parsimonious feature sets,medication-attribute linkage detection,crf-based multilayered sequence labeling,medical administrative data processing,support vector machines,linking medications,support vector machine-based binary classification | Structured support vector machine,Conditional random field,Sequence labeling,Binary classification,Pattern recognition,Computer science,Support vector machine,Clinical trial,Information extraction,Artificial intelligence,Relevance vector machine,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4673-4803-4 | 0 | 0.34 |
References | Authors | |
1 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Li | 1 | 65 | 6.98 |
| Haijun Zhai | 2 | 62 | 7.40 |
| Louise Deleger | 3 | 3 | 1.43 |
| Todd Lingren | 4 | 114 | 12.78 |
| Megan Kaiser | 5 | 92 | 7.44 |
| Laura Stoutenborough | 6 | 82 | 6.09 |
| Imre Solti | 7 | 337 | 23.36 |