Name
Abstract | ||
|---|---|---|
•We quantify the effectiveness of transformer-based models for clinical trial search. This is the first to have compared these methods under the same retrieval framework for a fair comparison.•We evaluate a broad selection of re-ranking models to uncover the role of pre-training corpus in evidence search for precision medicine.•Our evaluation points to a neural re-ranking system which achieves state-of-the-art results.•Our search system is fully automatic; its effectiveness does not rely on manual query reformulation strategies, nor problem-specific heuristic approaches.•With limited training data, leveraging the transfer learning inherent to transformer-based models makes the proposed model competitive to heuristic, manually optimised systems. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jbi.2020.103530 | Journal of Biomedical Informatics |
Keywords | DocType | Volume |
Clinical decision making,Document search,Information retrieval,Ranking functions,Learning-to-rank,Bidirectional transformer encoder,Natural language processing,Complex search,Precision medicine | Journal | 109 |
ISSN | Citations | PageRank |
1532-0464 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maciej Rybinski | 1 | 0 | 1.01 |
| Jerry Xu | 2 | 0 | 0.34 |
| Sarvnaz Karimi | 3 | 380 | 33.01 |