Authors: Siddhartha Jonnalagadda; Diana Petitti
Addresses: Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota 55902, USA ' Department of Biomedical Informatics, Arizona State University, Tempe, Phoenix 85281, USA
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).
Keywords: systematic review; distributional semantics; information retrieval; machine learning; biomedical literature; literature review; natural language processing; NLP; feature extraction; relevance feedback; drug reviews.
International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.1/2, pp.5 - 17
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 20 Feb 2013 *