Name
Abstract | ||
|---|---|---|
Clinical paper searching is a major task for clinical researchers to collect authoritative and up-to-date evidences to support their research works and clinical practices. Currently, this task needs huge amount of labor work. Researchers usually spend a lot of time searching on the online repository and iterating many times to get the final paper list. Systematic review is a special case, in which the paper searching process is a critical step. To address this challenge, this paper introduces a method to streamline the iterative paper searching process. It automatically selects the most probably matched papers, and then generates new search strategy. All the intermediate results are visualized based on the paper citation graph. It assembles technologies such as PageRank and Topic-based clustering to accelerate the paper searching tasks. The precision, recall, and execution time of the proposed method are then evaluated by comparing with published systematic reviews. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/SHTI190443 | Studies in Health Technology and Informatics |
Keywords | Field | DocType |
Paper,Bibliometrics,Literature Based Discovery | Data mining,Computer science | Conference |
Volume | ISSN | Citations |
264 | 0926-9630 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiqin Yu | 1 | 22 | 8.35 |
| Enliang Xu | 2 | 1 | 2.04 |
| Eryu Xia | 3 | 8 | 4.14 |
| He Huang | 4 | 0 | 1.01 |
| Bibo Hao | 5 | 0 | 2.37 |
| Shilei Zhang | 6 | 57 | 9.81 |