A Text-Mining Framework for Supporting Systematic Reviews
American Journal of Information Management
Volume 1, Issue 1, November 2016, Pages: 1-9
Received: Jul. 21, 2016; Accepted: Aug. 3, 2016; Published: Aug. 31, 2016
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Dingcheng Li, Department of Health Sciences Research, Mayo Clinic, Rochester, USA;Watson Health Cloud, IBM, Rochester, USA
Zhen Wang, Department of Health Sciences Research, Mayo Clinic, Rochester, USA;Robert D. and Patricia E. Kern Centre for the Science of Health Care Delivery, Mayo Clinic, Rochester, USA
Liwei Wang, Department of Health Sciences Research, Mayo Clinic, Rochester, USA
Sunghwan Sohn, Department of Health Sciences Research, Mayo Clinic, Rochester, USA
Feichen Shen, Department of Health Sciences Research, Mayo Clinic, Rochester, USA
Mohammad Hassan Murad, Robert D. and Patricia E. Kern Centre for the Science of Health Care Delivery, Mayo Clinic, Rochester, USA;Division of Preventive Medicine, Mayo Clinic, Rochester, USA
Hongfang Liu, Department of Health Sciences Research, Mayo Clinic, Rochester, USA
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Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.
Systematic Review, Text Mining, Topic Modeling, Keyword Relevance, Indexed-Term Relevance, Topic Relevance, Data Mining
To cite this article
Dingcheng Li, Zhen Wang, Liwei Wang, Sunghwan Sohn, Feichen Shen, Mohammad Hassan Murad, Hongfang Liu, A Text-Mining Framework for Supporting Systematic Reviews, American Journal of Information Management. Vol. 1, No. 1, 2016, pp. 1-9. doi: 10.11648/j.infomgmt.20160101.11
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