2 Davis Drive, Research Triangle Park, NC 27709
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SWIFT-Review (SWIFT is an acronym for “Sciome Workbench for Interactive computer-Facilitated Text-mining”) is a freely available interactive workbench which provides numerous tools to assist with problem formulation and literature prioritization. SWIFT-Review puts the systematic review expert in the driver’s seat by providing several features that can be used to search, categorize, and prioritize large (or small) bodies of literature in an interactive manner. SWIFT-Review utilizes newly developed statistical text mining and machine learning methods that allow users to uncover over-represented topics within the literature corpus and to rank order documents for manual screening.

For more information about SWIFT-Review, and other Sciome products and services please contact us at swift-review@sciome.com.

SWIFT-Review Graphic

Publications and Presentations

  • Glynn C, Tokdar S, Banks D, Howard B (2018). Bayesian Analysis of Dynamic Linear Topic Models. Bayesian Anal, advance publication, 14 April 2018 doi:10.1214/18-BA1100. https://projecteuclid.org/euclid.ba/1523671249.
  • Howard BE, Tandon A, Phillips J, Shah MR, Mav D, Shah RR (2017). “Using Machine Learning and SWIFT-Active Screener to Reduce the Expense of Evidence Based Toxicology.” Poster presentation at GEMS Fall Meeting 2017, RTP, NC.
  • Kiros BA, Howard BE, Holmgren S, Baker N, Cleland J, Walker VR, Antonic A, Devito MJ, Kwiatkowski CF, Bolden AL, Pelch KE, Zoeller T, Thayer KA (2017). “Use of Text-mining and Machine Learning Approaches to Conduct a Rapid Literature Survey on Environmental Chemicals and the Thyroid.” Mendeley Data, v1.
  • Howard BE, Phillips J, Miller K, Tandon A, Mav D, Shah MR, Holmgren S, Pelch KE, Walker V, Rooney AA, Macleod M, Shah R, Thayer, K (2016). SWIFT-Review: A Text-mining Workbench for Systematic Review. Systematic Reviews, 5:87.
  • Glynn C, Tokdar S, Banks D, Howard B (2015). “Fully Bayesian Inference for a Dynamic Linear Topic Model.” Poster presentation at the 10th Conference on Bayesian Nonparametrics, Raleigh, NC.
  • Walker V, Holmgren S, Thayer K, Rooney A, Pelch K, Macleod M, Currie G, Sena E, Sherratt N, Rice A, Howard B, Shah R (2015). “Evaluation of the priority ranking capabilities of SWIFT (Sciome Workbench for Interactive, Computer Facilitated Text-mining) software.” 23rd Cochrane Colloquium, Vienna, AT.
  • Thayer K, Howard B, Holmgren S, Pelch K, Walker V, Lunn R, Shah R (2015). “Application of text mining and machine learning for problem formulation in systematic reviews.” 23rd Cochrane Colloquium, Vienna, AT.
  • Howard BE, Phillips J, Holmgren S, Thayer K, Shah R (2015). “Enhancing literature review with SWIFT.” Webinar at National Institute of Environmental Health Sciences (NIEHS), RTP, NC.
  • Howard BE, Phillips J, Holmgren S, Thayer K, Shah R (2015). “Endocrine disrupting chemicals: using SWIFT text mining tool to assess the current state of the science.” Webinar at National Institute of Environmental Health Sciences (NIEHS), RTP, NC.
  • Howard BE, Phillips J, Holmgren S, Thayer K, Shah R (2015). “Using the SWIFT text-mining tool to assess the current state of the science for carcinogens and other chemicals.” Webinar at National Institute of Environmental Health Sciences (NIEHS), RTP, NC.
  • Walker V, Holmgren S, Pelch K, Howard B, Shah R, Thayer K, Rooney A (2015). “Problem formulation of complex environmental health questions: utilizing text mining to address challenges of a literature-based evaluation of transgenerational health effects.” Poster presentation at the Society of Toxicology’s (SOT) 54th Annual Meeting and ToxExpo, San Diego, CA.
  • Phillips J, Howard BE, Shah R (2014). “Scientific text extraction using FIDDLE: a foundation for accurate literature mining.” Poster presentation at the Toxcast Data Summit, RTP, NC.
  • Howard BE, Shah R, Walker V, Pelch K, Holmgren S, Thayer K (2014). “Use of text-mining and machine learning to prioritize the results of a complex literature search.” Poster presentation at the Society of Toxicology?s (SOT) 53rd Annual Meeting and ToxExpo, Phoenix, AZ.
  • Howard BE, Shah R (2013). “Augmenting the Report on Carcinogens with Automation, Literature Mining and Full-Text Search.” National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC.
  • Howard BE, Shah R (2013). “Enhancing Literature Review with Text Mining and Machine Learning.” National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC.
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