Document Type
Conference Proceeding
Publication Date
2016
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 semisupervised 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.
Recommended Citation
Timsina, P., Liu, J., El-Gayar, O., & Shang, Y. (2016, January). Using semi-supervised learning for the creation of medical systematic review: An exploratory analysis. In 2016 49th Hawaii International Conference on System Sciences (HICSS) (pp. 1195-1203). IEEE.