The full text of this article
A new iterative method to reduce workload in systematic review process
by Siddhartha Jonnalagadda; Diana Petitti
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 6, No. 1/2, 2013
Abstract: High cost for systematic review of biomedical literature has generated interest in decreasing overall workload. This can be done by applying natural language processing techniques to 'automate' the classification of publications that are potentially relevant for a given question. Existing solutions need training using a specific supervised machine-learning algorithm and feature-extraction system separately for each systematic review. We propose a system that only uses the input and feedback of human reviewers during the course of review. As the reviewers classify articles, the query is modified using a simple relevance feedback algorithm, and the semantically closest document to the query is presented. An evaluation of our approach was performed using a set of 15 published drug systematic reviews. The number of articles that needed to be reviewed was substantially reduced (ranging from 6% to 30% for a 95% recall).
Online publication date: Wed, 20-Feb-2013
is only available to individual subscribers or to users at subscribing institutions.
Go to Inderscience Online Journals to access the Full Text of this article.
Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.
Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Biology and Drug Design (IJCBDD):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable).
See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org