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

我们假设，一个灵活的，可配置的自动信息学工具套件可以显着减少 需要作出努力进行系统审查，同时保持甚至提高质量。为了验证这一 假设，我们建议： 目标1.为了扩展我们对自动RCT标签的研究，以包括其他研究类型， 提供公共资源。 A）将创建机器学习模型，自动将概率估计分配给三种类型的 由系统评价者广泛检查的观察性研究。 B）将对新近发表的PubMed文章前瞻性评价RCT和其他标签。 C）所有PubMed文章将自动标记为RCT、队列、病例对照和横断面研究 并在链接到公共查询接口的公共数据集中进行注释。用户还将收到文章的标签 非PubMed数据来源。 目标2.评估系统评价者使用我们工具时的性能和可用性 在现场条件下。 （A）将对工具进行定制和整合，以便利实地评估。 B）三阶段评估：1. Metta和RCT Tagger性能的回顾性评价。2.实时 “影子”。3.前瞻性对照研究。 目标3.为了识别其他临床试验文章，在已发表的系统性综述后出现， 已完成，与审查主题相关。 目标4。识别与特定ClinicalTrials.gov注册试验相关的出版物。 目标5。开发和评估新的机器学习方法和工具，以促进快速 新系统性综述主题的证据范围。 A）将制定方法，根据文章与拟议的新系统的相关性对文章进行排名。 复习题。 B）将创建一个范围界定工具，显示按预测相关性排序的文章，并标记研究设计 属性、样本量和科克伦偏倚风险估计。 拟议的研究将推进系统评价撰写过程中早期步骤的自动化， 从而加强循证医学，并将最佳实践纳入临床护理。

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.