Published in

Public Library of Science, PLoS ONE, 1(9), p. e86277, 2014

DOI: 10.1371/journal.pone.0086277

Links

Tools

Export citation

Search in Google Scholar

Feature Engineering and a Proposed Decision-Support System for Systematic Reviewers of Medical Evidence

Journal article published in 2014 by Tanja Bekhuis ORCID, Eugene Tseytlin, Kevin J. Mitchell, Dina Demner-Fushman
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

Evidence-based medicine depends on the timely synthesis of research findings. An important source of synthesized evidence resides in systematic reviews. However, a bottleneck in review production involves dual screening of citations with titles and abstracts to find eligible studies. For this research, we tested the effect of various kinds of textual information (features) on performance of a machine learning classifier. Based on our findings, we propose an automated system to reduce screeing burden, as well as offer quality assurance.