SWIFT-Active Screener is a web-based, collaborative systematic review software application. Active Screener was designed to be easy-to-use, incorporating a simple, but powerful, graphical user interface with rich project status updates. What makes Active Screener special, however, is its behind-the-scenes application of state-of-the-art statistical models designed to save screeners time and effort by automatically prioritizing articles as they are reviewed, using user feedback to push the most relevant articles to the top of the list.
How Active Screener Works for You
As screening proceeds, reviewers include or exclude articles while an underlying statistical model automatically computes which of the remaining unscreened documents are most likely to be relevant. This “Active Learning” model is continuously updated during screening, improving its performance with each article reviewed. Meanwhile, a separate statistical model estimates the number of relevant articles remaining in the unscreened document list. Together, the combination of the two models allows users to screen relevant documents sooner and provides them with accurate feedback about their progress. Using this approach, the vast majority of relevant articles can often be discovered after reviewing only a fraction of the total number of articles. This can result in a significant time and cost savings for you and your team, especially for large projects.
For more information about SWIFT-Active Screener and other Sciome products and services, please contact us at swift-activescreener@sciome.com.
User-Friendly Project Creation
Hit the ground running! Quickly and easily create screening projects, including data extraction questions to fit most any study design, without the need to sit through hours of tutorial videos or read dozens of pages in a user manual.
Real Time Team Collaboration
Active Screener allows teams to collaborate using a web-based interface which will enable your group to perform a systematic review anywhere, anytime. This flexibility allows for easy progress sharing and makes team communication easier to manage.
Project Progress
Active Screener’s project management tools make it easy to view your team’s progress as your systematic review is underway.
Conflict Resolution
Active Screener tracks screening conflicts and allows you to resolve them quickly and efficiently so that you can complete your review without delay.
Data Integration
Active Screener integrates with all of the systematic review tools that you already use. It accepts imports from many of the most popular bibliographic databases and reference curation platforms including EndNote, Mendeley, Zotaro, PubMed, and SWIFT-Review. Results from screening activities in Active Screener can also be exported in standard data formats compatible with a wide range of applications including EndNote, Mendeley, Zotaro, PubMed, HAWC and Excel.
Publications, Presentations, Patents
- Wang A, Chappell GA, Varghese A, Howard BE, Schmidt L, Silva R, Arroyave W, Lunn, R, Rooney AA. (2024). “An Evaluation of the Performance of Survey Sampling in Systematic Evidence Mapping of Cancer Mechanistic Evidence: Polycyclic aromatic hydrocarbons (PAHs) and Key Characteristics of Carcinogens (KCCs) as a Case Study.” Poster presentation at the Society of Toxicology 63rd Annual Meeting and ToxExpo, Salt Lake City, UT.
- Howard BE, Norman CR, Tandon A, Shah R, Olker J, Hoff D (2023). “After Correcting for “Concept Drift,” Deep-Learning Methods Can Now Achieve Human-Level Performance When Predicting Article Exclusion Reasons During ECOTOXicology Knowledgebase Curation.” Poster presentation at the Society of Toxicology 62nd Annual Meeting and ToxExpo, Nashville, TN.
- Howard BE, Arroyave W, Schmidt L, Elmore B, Bisson W, Atwood S, Sethi M, Ewens A, Lunn R, Shah R, Merrick B.A., Wang A. (2023). “Use of Survey Sampling to Develop an Evidence Map of Carcinogenic Mechanistic Information for Polycyclic Aromatic Hydrocarbons (PAHs) and Key Characteristics of Carcinogens (KCCs).” Poster presentation at the Society of Toxicology 62nd Annual Meeting and ToxExpo, Nashville, TN.
- Howard BE, Norman CR, Tandon A, Shah R, Olker J, Hoff D (2022). “Improving the Efficiency of Literature Identification for the ECOTOXicology Knowledgebase Using Deep Learning.” Poster presentation at the Society of Toxicology 61st Annual Meeting and ToxExpo, San Diego, CA.
- Elmore R, Schmidt L, Lam J, Howard B, Tandon A, Norman C, Phillips J, Shah M, Patel S, Albert T, Taxman D, Shah R (2021). “Risk and Protective Factors in the COVID-19 Pandemic: A rapid Evidence Map (rEM).” Poster presentation at the Society of Toxicology 60th Annual Meeting and ToxExpo, Virtual.
- Howard BE, Norman CR, Tandon A, Shah R, Olker J, Elonen C, Hoff D (2021). “A Web-based Literature Identification Platform for the ECOTOXicology Knowledgebase, Powered by Deep Learning.” Poster presentation at the Society of Toxicology 60th Annual Meeting and ToxExpo, Virtual.
- Howard BE, Norman C, Tandon A, Shah R, Olker J, Elonen C, Hoff D (2020). “A Web-based Literature Identification Platform for the ECOTOXicology Knowledgebase, Powered by Deep Learning.” Platform presentation given at the SETAC North America 41st Annual Meeting, Virtual.
- Elmore R, Schmidt L, Lam J, Howard BE, Tandon A, Norman C, Phillips J, Shah M, Patel S, Albert T, Taxman DJ, Shah RR; (2020). “Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map.” Frontiers in Public Health 8 (787). doi: 10.3389/fpub.2020.582205.
- Howard BE, Phillips J, Tandon A, Maharana A, Elmore R, Mav D, Sedykh A, Thayer K, Merrick A, Walker V, Rooney A, Shah RR (2020). “SWIFT-Active Screener: accelerated document screening through Active Learning and integrated recall estimation.” Environment International 138 May 2020, 105623. doi: 10.1016/j.envint.2020.105623.
- Lam J, Howard BE, Thayer K, Shah RR (2020). “Evidence Mapping Health Outcomes: Rapid vs. Traditional Approaches.” Poster presentation at the Society of Toxicology 59th Annual Meeting and ToxExpo, Virtual.
- Howard B, Tandon A, Maharana A, Shah R (2019). “Using SWIFT-Active Screener to reduce the expense of evidence-based toxicology.” Presentation at the Cochrane Colloquium Santiago, Virtual.
- Lam J, Howard B, Bautz D, Anderson L, Tandon A, Maharana A, Shah R (2019). “Rapid Evidence Mapping for Environmental Toxicology.” Poster presentation at the 8th Annual Meeting of the American Society for Cellular and Computational Toxicology (ASCCT), Gaithersburg, MD.
- Lam J, Howard B, Thayer K, Shah R (2019). Low-Calorie Sweeteners and Health Outcomes: A Demonstration of rapid Evidence Mapping (rEM). Environment International 2019 Feb; 123:451-458. doi: 10.1016/j.envint.2018.11.070.
- Lam J, Howard BE, Thayer K, Shah RR (2019). “Rapid Evidence Mapping (rEM) in Environmental Health: Low-Calorie Sweeteners and Human Health. Poster presented at the Society of Toxicology 58th Annual Meeting and ToxExpo, Baltimore, MD.
- Lam J, Howard B, Tandon A, Maharana A, Elmore R, Merrick B, Shah R, Wolfe M (2019). “Rapid Evidence Mapping (rEM): Feminine Care Product Case Study.” Poster presented at the Society of Toxicology 58th Annual Meeting and ToxExpo, Baltimore, MD.
- Howard BE, Shah R, Mav D, Miller K (2019). “Methods and systems to estimate recall while screening an ordered list of bibliographic references.” Patent – US20190080002A1.
- Howard BE, Tandon A, Phillips J, Maharana A, Shah RR (2018). “Research Update: Using SWIFT-Active Screener to Reduce the Expense of Evidence Based Toxicology.” Poster presentation at the Fall meeting of the North Carolina Genetics and Environmental Mutagenesis Society (GEMS), RTP, NC.
- Lam J, Howard BE, Thayer K, Shah RR (2018). “Rapid Evidence Mapping for Health: A Case Study. Poster presentation at the Fall meeting of the North Carolina Genetics and Environmental Mutagenesis Society (GEMS), RTP, NC.
- Lam J, Howard B, Tandon A, Maharana A, Elmore R, Merrick B, Shah R, Wolfe M (2018). “Rapid Evidence Mapping (rEM): Feminine Care Product Case Study.” Poster presentation at the Fall meeting of the North Carolina Genetics and Environmental Mutagenesis Society (GEMS), RTP, NC.
- Howard BE, Tandon A, Phillips J, Shah M, Mav D, Shah R (2018). “Using Machine-Learning and SWIFT-Active Screener to Reduce the Expense of Evidence-Based Toxicology.” Poster presented at the Society of Toxicology 57th Annual Meeting and ToxExpo, San Antonio, TX.
- Lam J, Howard BE, Tandon A, Maharana A, Shah R (2018). “Rapid Evidence Mapping for Environmental Health.” Poster presented at the Society of Toxicology 57th Annual Meeting and ToxExpo, San Antonio, TX.
- Glynn C, Tokdar ST, Howard B, Banks, D (2018). “Bayesian Analysis of Dynamic Linear Topic Models.” Bayesian Analysis.
- Howard BE, Tandon A, Phillips J, Shah MR, Mav D, Shah RR (2017). “SWIFT-Active Screener: Faster Document Screening with Data Analytics.” Poster presentation at the Fall meeting of the North Carolina Genetics and Environmental Mutagenesis Society (GEMS), RTP, NC.
- 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 the Genetics and Environmental Mutagenesis 2017 Annual Fall Meeting, RTP, NC
- Howard BE, Miller K, Phillips J, Shah M, Mav D, Thayer K, Shah R (2017). “Enabling Evidence-Based Toxicology with SWIFT Active Screener.” Poster presentation at the Society of Toxicology’s 56th Annual Meeting and ToxExpo, Baltimore, MD.
- Miller K, Howard BE, Phillips J, Shah MR, Mav D, Thayer K, Shah RR (2016). “SWIFT-Active Screener: Reducing Literature Screening Effort Through Machine Learning for Systematic Reviews.” 24th Cochrane Colloquium. Seoul, South Korea.
- Howard BE, Miller K, Shah RR (2016). “SWIFT-Active Screener (A Brief Introduction).” National Institute of Environmental Health Sciences (NIEHS). RTP, NC.
- Miller K, Howard BE, Phillips J, Shah MR, Mav D, Thayer K, Shah RR (2016). “Updating Systematic Reviews with Active Learning.” 24th Cochrane Colloquium. Seoul, South Korea.
- Miller K, Howard BE, Phillips J, Shah MR, Mav D, Shah RR (2016). “SWIFT-Active Screener: Reducing Literature Screening Effort Through Machine Learning for Systematic Reviews.” Poster presentation at the Society of Toxicology’s 55th Annual Meeting and ToxExpo, New Orleans, LA.
Current Users Include
EBTC has been an active user of SWIFT Active Screener since 2016 and we have continued providing feedback on features and functionality and using the product. The machine learning capabilities implemented in SWIFT Active Screener have allowed us to complete our projects in half the time compared to manual screening. Moreover, in a large project with over 7,000 references we have manually screened the papers that the SWIFT algorithm had excluded as not relevant, and we have not found even one relevant paper! We greatly enjoy working with the Sciome team, who are very supportive and knowledgeable. In fact, it feels like they are a part of our extended research team.
Our entire team has been very impressed with using the Active Screener software. It has allowed us to drastically reduce the manual effort involved with screening citations in support of our reviews. The software was very effective at its prediction of article relevance. Complementing expert screeners with artificial intelligence to support citation screening is truly the direction in which systematic reviews should go in the future.
Active Screener turned the task of reviewing almost 8,000 references from overwhelming to manageable, both in terms of time and in eliminating human error introduced by hand-screening references. The built-in features for adding reviewers, keeping metrics of added and removed references, and conflict reports between screeners make it an ideal, transparent first step in systematic review. As a student beginning my first systematic review, I find the progress tracker mentally helpful too. The Sciome support team goes above and beyond to help with any questions or concerns, and are always working to improve the user experience. I recommend it to anyone undertaking a systematic review.
Our group has discovered great value in using SWIFT Active Screener to assist with screening large numbers of references for systematic review work. This software helps to relieve the burden of manual screening, and its automatic reprioritisation of references to be screened based on their predicted relevance is most convenient. We really appreciate the support and expertise of the Sciome team who are available to offer ongoing support for our review efforts.
As a consultant for large healthcare organizations, I work with physicians to develop evidence-informed clinical recommendations in the area of cardiac medicine, clinical oncology, and family practice. The body of evidence in these rapidly developing fields is vast, and having reliable, relevant and timely information is absolutely essential. I incorporated SWIFT Active Screener software early in 2020. Active Screener software enabled me to process thousands of studies in a fraction of the time. Additionally, I validated that the titles excluded by machine learning were correctly processed, so I feel confident that the software is doing its job. The team at Sciome provides excellent customer service and support. I can absolutely say that SWIFT Active Screener software is now a fundamental tool in my evidence toolkit.
SWIFT Active Screener is a great application for performing literature reviews. It is intuitive to use, and the learning curve is very fast. Teamwork is facilitated and article review time is significantly reduced.
Another important aspect is that the SWIFT team is very committed to the development of the application and to the users. I strongly recommend SWIFT Active Screener.
Another important aspect is that the SWIFT team is very committed to the development of the application and to the users. I strongly recommend SWIFT Active Screener.
