Advanced analytics for the automation of medical systematic reviews View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-04

AUTHORS

Prem Timsina, Jun Liu, Omar El-Gayar

ABSTRACT

While 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. More... »

PAGES

237-252

References to SciGraph publications

  • 1995-09. Support-vector networks in MACHINE LEARNING
  • 1998. Text categorization with Support Vector Machines: Learning with many relevant features in MACHINE LEARNING: ECML-98
  • 2010-12. Semi-automated screening of biomedical citations for systematic reviews in BMC BIOINFORMATICS
  • 2010-07-07. Data Mining for Imbalanced Datasets: An Overview in DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK
  • 2014-12. Systematic review automation technologies in SYSTEMATIC REVIEWS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10796-015-9589-7

    DOI

    http://dx.doi.org/10.1007/s10796-015-9589-7

    DIMENSIONS

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