Name
Title | ||
|---|---|---|
Using Semi-Supervised Learning For The Creation Of Medical Systematic Review: An Exploratory Analysis |
Abstract | ||
|---|---|---|
In this research, we explore semi-supervised learning based classifiers to identify articles that can be included when creating medical systematic reviews (SRs). Specifically, we perform comparative study of various semi-supervised learning algorithm, and identify the best technique that is suited for SRs creation. We also aim to identify whether semi supervised learning technique with few labeled samples produce meaningful work saving for SRs creation. Through an empirical study, we demonstrate that semi-supervised classifiers are viable for selecting articles for systematic reviews and situations when only a few numbers of training samples are available. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/HICSS.2016.151 | PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016) |
Field | DocType | ISSN |
Semi-supervised learning,Instance-based learning,Systematic review,Computer science,Support vector machine,Supervised learning,Unsupervised learning,Artificial intelligence,Statistical classification,Machine learning,Learning classifier system | Conference | 1060-3425 |
Citations | PageRank | References |
1 | 0.38 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Prem Timsina | 1 | 24 | 4.24 |
| Jun Liu | 2 | 13 | 4.79 |
| Omar El-Gayar | 3 | 136 | 19.64 |
| Yanyan Shang | 4 | 1 | 0.72 |