Name
Title | ||
|---|---|---|
Supervised line attention for tumor attribute classification from pathology reports: Higher performance with less data |
Abstract | ||
|---|---|---|
•Annotating clinical documents is costly and time consuming.•Performance of natural language processing methods is limited in small data settings.•Enriching annotations with location information can help to achieve sample efficiency.•Machine learning methods trained on enriched annotations outperform existing method. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jbi.2021.103872 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Natural Language Processing,Cancer,Information Extraction,Pathology,EHR | Journal | 122 |
ISSN | Citations | PageRank |
1532-0464 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas Altieri | 1 | 0 | 0.34 |
| Briton Park | 2 | 0 | 0.34 |
| Mara Olson | 3 | 0 | 0.34 |
| John DeNero | 4 | 588 | 28.98 |
| Anobel Y Odisho | 5 | 1 | 1.03 |
| Bin Yu | 6 | 1984 | 241.03 |