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Information Systems Frontiers
Erschienen in: Information Systems Frontiers 2/2016

01.04.2016

Advanced analytics for the automation of medical systematic reviews

verfasst von: Prem Timsina, Jun Liu, Omar El-Gayar

Erschienen in: Information Systems Frontiers | Ausgabe 2/2016

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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.
Vorheriger Artikel Big data and analytics in healthcare: Introduction to the special section
Nächster Artikel A study on the antecedents of healthcare information protection intention

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Metadaten
Titel
Advanced analytics for the automation of medical systematic reviews
verfasst von
Prem Timsina
Jun Liu
Omar El-Gayar
Publikationsdatum
01.04.2016
Verlag
Springer US
Erschienen in
Information Systems Frontiers / Ausgabe 2/2016
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI
https://doi.org/10.1007/s10796-015-9589-7

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