Advanced analytics for the automation of medical systematic reviews

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• 作者：Prem Timsina ; Jun Liu ; Omar El-Gayar
• 关键词：Healthcare ; Medical systematic reviews ; analytics ; Support vector machines
• 刊名：Information Systems Frontiers
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：18
• 期：2
• 页码：237-252
• 全文大小：571 KB
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• 作者单位：Prem Timsina (1)
  Jun Liu (1)
  Omar El-Gayar (1)
  
  1. College of Business and Information Systems, Dakota State University, 820 N. Washington Avenue, Madison, SD, 57042, USA
  
• 刊物类别：Business and Economics
• 刊物主题：Economics
  Business Information Systems
  Management of Computing and Information Systems
  Systems Theory and Control
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1572-9419

文摘

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.