Semi-Automating Data Extraction for Systematic Reviews

摘要​用于系统评审的半自动数据提取(​续订) 循证医学(EBM)的目标是利用所有可用的证据为患者护理提供信息。 在实践中实现这一目标需要获得简明、全面和最新的 与特定临床问题相关的证据的结构化摘要。系统化 生物医学文献综述旨在提供这样的总结，并且是 循证医学武器库和更广泛的现代医学。然而，这样的审查是非常 指挥起来很费力。此外，由于生物医学文献的迅速膨胀 随着新证据的出现，它们往往很快就会过时。这些因素阻碍了 循证护理的实践。 在这项更新建议中，我们寻求继续我们的开创性努力， 评估和部署新型机器学习(ML)和自然语言处理(NLP) 自动化或半自动化证据合成过程的方法。这将延长我们的 创新和成功的努力在开发RobotReviewer和相关技术方面 目前的拨款。具体地说，对于这次更新，我们建议从临床提取 从个别试验的显著数据元素到跨试验的这些元素的合成。我们的第一次 目标是扩展我们的ML和NLP模型，以生产(作为一个可交付件)公开可用的、 持续自动更新的半结构化证据数据库，包括 提取了所有证据的数据，包括已发布和未发布的。未发表的试验将是 通过试验登记处确认。 以这个最新的证据仓库为起点，我们提出了尖端的ML 以及将自动生成证据合成初稿的NLP模型。更多 具体地说，我们提出了新的神经跨文档摘要模型，该模型将 关于我们现有模型自动提取的半结构化信息，此外 来写文章。这些模型将部署在名为RobotReviewer的新版本中 RobotReviewerLive，旨在成为“活的”系统评估的原型。严谨地 评估提出的方法创新的实际效用，我们将对其进行试点使用 以支持真实的、持续的、模范的生活评论。

Summary ​Semi-Automating Data Extraction for Systematic Reviews (​Renewal) Evidence-based Medicine (EBM) aims to inform patient care using all available evidence. Realizing this aim in practice would require access to concise, comprehensive, and up-to-date structured summaries of the evidence relevant to a particular clinical question. Systematic reviews of biomedical literature aim to provide such summaries, and are a critical component of the EBM arsenal and modern medicine more generally. However, such reviews are extremely laborious to conduct. Furthermore, owing to the rapid expansion of the biomedical literature base, they tend to go out of date quickly as new evidence emerges. These factors hinder the practice of evidence-based care. In this renewal proposal, we seek to continue our ground-breaking efforts on developing, evaluating, and deploying novel machine learning (ML) and natural language processing (NLP) methods to automate or semi-automate the evidence synthesis process. This will extend our innovative and successful efforts developing RobotReviewer and related technologies under the current grant. Concretely, for this renewal we propose to move from extraction of clinically salient data elements from individual trials to synthesis of these elements across trials. Our first aim is to extend our ML and NLP models to produce (as one deliverable) a publicly available, continuously and automatically updated semi-structured evidence database, comprising extracted data for all evidence, both published and unpublished. Unpublished trials will be identified via trial registries. Taking this up-to-date evidence repository as a starting point, we then propose cutting-edge ML and NLP models that will generate first drafts of evidence syntheses, automatically. More specifically we propose novel neural cross-document summarization models that will capitalize on the semi-structured information automatically extracted by our existing models, in addition to article texts. These models will be deployed in a new version of RobotReviewer, called RobotReviewerLive, intended to be a prototype for “living” systematic reviews. To rigorously evaluate the practical utility of the proposed methodological innovations, we will pilot their use to support real, ongoing, exemplar living reviews.