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Title:Comparing articles identified as Randomized Controlled Trials: MEDLINE, Cochrane, and the RCT Tagger
Author(s):Kansara, Yogeshwar; Hoang, Linh; Schneider, Jodi
Subject(s):Machine Learning
Error Analysis
Randomized Controlled Trials
evidence-based clinical practice
Abstract:Randomized Controlled Trials (RCTs) are considered the gold standard of medical knowledge about treatment effects. RCTs are used in evidence-based clinical practice and for the production of systematic reviews. Determining whether or not an article is a RCT, thus, can be useful for several search applications, including supporting clinicians in finding high-quality information, and providing high-specificity searches for systematic searching. In this study, we pilot a methodology for evaluating a machine learning tool called Tagger, that aims to distinguish Randomized Controlled Trials reports from other medical literature. The goal of our evaluation is to assess the feasibility of using the tool in real-life systematic review projects by examining Tagger's technical performance in identifying RCT reports included in a sample of 895 systematic reviews. We present the evaluation results and discuss possible improvements for the tool.
Issue Date:2018-10-31
Citation Info:Kansara Y, Hoang LK, Schneider J. Comparing articles identified as Randomized Controlled Trials: MEDLINE, Cochrane, and the RCT Tagger. In The iSchool at Illinois 2018 Research Showcase; Illinois. 2018.
Genre:Conference Poster
Type:Text
Other
Language:English
URI:http://hdl.handle.net/2142/102074
Date Available in IDEALS:2018-11-25


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