Paper Info
Title
Advanced analytics for the automation of medical systematic reviews
Abstract
AbstractWhile systematic reviews (SRs) are positioned as an essential element of modern evidence-based medical practice, the creation and update of these reviews is resource intensive. In this research, we propose to leverage advanced analytics techniques for automatically classifying articles for inclusion and exclusion for systematic reviews. Specifically, we used soft-margin polynomial Support Vector Machine (SVM) as a classifier, exploited Unified Medical Language Systems (UMLS) for medical terms extraction, and examined various techniques to resolve the class imbalance issue. Through an empirical study, we demonstrated that soft-margin polynomial SVM achieves better classification performance than the existing algorithms used in current research, and the performance of the classifier can be further improved by using UMLS to identify medical terms in articles and applying re-sampling methods to resolve the class imbalance issue.
Year
DOI
Venue
2016
10.1007/s10796-015-9589-7
Periodicals
Keywords
Field
DocType
Healthcare, Medical systematic reviews, analytics, Support vector machines
Data mining,Systematic review,Polynomial,Computer science,Support vector machine,Automation,Artificial intelligence,Classifier (linguistics),Analytics,Unified Medical Language System,Machine learning,Empirical research
Journal
Volume
Issue
ISSN
18
2
1387-3326
Citations 
PageRank 
References 
5
0.53
13
Authors
3
Name
Order
Citations
PageRank
Prem Timsina1244.24
Jun Liu2134.79
Omar El-Gayar313619.64