Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine

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We hypothesize that a flexible, configurable suite of automated informatics tools can reduce significantly the effort needed to generate systematic reviews while maintaining or even improving their quality. To test this hypothesis, we propose: Aim 1. To extend our research on automated RCT tagging to include additional study types and provide public resources. A) Machine learning models will be created that automatically assign probability estimates to three types of observational studies that are widely examined by systematic reviewers. B) The RCT and other taggers will be evaluated prospectively for newly published PubMed articles. C) All PubMed articles will be automatically tagged for RCT, cohort, case-control and cross-sectional studies and annotated in a public dataset linked to a public query interface. Users will also receive tags for articles from non-PubMed data sources on demand. Aim 2. To evaluate the performance and usability of our tools when used by systematic reviewers under field conditions. A) The tools will be customized and integrated to facilitate field evaluation. B) A three-stage evaluation: 1. Retrospective evaluation of Metta and RCT Tagger performance. 2. Real-time “shadowing”. 3. Prospective controlled study. Aim 3. To identify additional clinical trial articles, appearing after a published systematic review was completed, that are relevant to the review topic. Aim 4. To identify publications related to specific ClinicalTrials.gov registered trials. Aim 5. To develop and evaluate new machine learning methods and tools that will facilitate rapid evidence scoping for new systematic review topics. A) Methods will be developed for ranking articles with respect to their relevance to a proposed new systematic review topic. B) A scoping tool will be created that displays articles ranked by predicted relevance, tagged with study design attributes, sample sizes, and Cochrane risk of bias estimates. The proposed studies will advance the automation of early steps in the process of writing systematic reviews, and thereby enhance evidence-based medicine and the incorporation of best practices into clinical care.