A Comprehensive Data-driven Platform for Acute Ischemic Stroke Patient Outcomes

项目总结 一系列重要决定，从报销到监管许可，再到医生和患者的决定- 制作，取决于临床发表。临床发表是循证医学的主要机制 因此，决策和文献综述是主要的综合临床结果比较 方法论。然而，目前的文献综述方法是过时的、杂乱无章的、杂乱无章的 只有在数百小时的文献工作中才能访问和比较可能挽救生命的数据 回顾一下。尤其是考虑到技术驱动的数据管理的增长，当前的 组合临床结果数据从根本上无法向普通医疗受众传达 给出治疗效果，以透明、全面和可更新的形式，典型的荟萃分析是 甚至无法证明其在现有索引上的搜索覆盖率。 这个问题已经被许多组织认识到，从NIH的数据信息学工作组 (DIWG)到AAAS到美国国家科学院、工程院、医学院，它们都公开 指出，技术驱动的协调和数据可视化对于有效分享研究是必要的。 然而，可搜索性、可视化和协调性的努力尚未渗透到医学出版中。 DIWG表示：“进行生物医学研究的技术和方法发生了巨大的变化。 将科学生产力的瓶颈从数据生产转移到数据管理、通信和 对数据的解读。我们同意，临床科学中最大的瓶颈实际上与 我们认为，这是由于在出版中没有充分采用新技术， 特别是展示全领域数据的交互式可视化方法和数据收集的自动化方法。 我们的愿景是创建一个全面研究、不断更新、易于理解的平台 以专家设计的、自动化的方式在科学出版物之间传播关键数据 从现有出版物中提取数据。在中风中首次亮相-因为我们之前在 现场-我们已经实现了交互式、可视化元分析和部分自动化数据的概念验证 从PDF中提取。现在，我们建议自动化元分析的基础，即搜索/包含 流程，通过数据分析和预测建模，并将我们的平台扩展到包括所有研究 与中风研究相关。如果成功，我们的项目也将提供中风领域的研究工具 使我们能够创造方法，使跨所有医学学科的扩展成为可能。

PROJECT SUMMARY A wide range of vital decisions, from reimbursement to regulatory clearance to physician and patient decision- making, depend on clinical publication. Clinical publications are the primary mechanism of evidence-based decisions, and literature reviews are thus the primary comprehensive clinical outcome comparison methodology. However, current literature review methods are outdated, unstructured, and disorganized, and potentially lifesaving data are accessible and comparable only with hundreds of hours of work in literature reviewing. Especially given the growth of technology-driven data management, the current paradigm of combining clinical outcome data fundamentally fails to communicate to general medical audiences whether any given therapy works, in transparent, comprehensive, and updatable forms, and the typical meta-analysis is unable to even demonstrate level of coverage of its search across existing indices. This problem has been recognized by many organizations, from the NIH’s Data Informatics Working Group (DIWG) to the AAAS to The National Academies of Sciences, Engineering, Medicine, which have all publicly stated that tech-driven harmonization and data visualization are necessary to effectively share research. However, searchability, visualization, and harmonization efforts have not yet permeated medical publishing. The DIWG stated that: “The colossal changes in technologies and methods for doing biomedical research have shifted the bottleneck in science productivity from data production to data management, communication, and data interpretation.” We agree that the greatest bottleneck in clinical sciences are in fact related to communication, and we believe that it is due to insufficient adoption of novel technologies in publishing, especially interactive visual methods of presenting field-wide data and automated methods of data gathering. Our vision is to create a comprehensively researched, constantly updated, easily digestible platform for dissemination of crucial data presented among scientific publications based on expert-designed, automated data extraction from existing publications. After debuting in stroke—because of our previous experience in the field—we have achieved proof-of-concept for interactive, visual meta-analyses and for partially-automated data extraction from PDFs. Now, we propose to automate the foundation of meta-analysis, the search/inclusion process, through data analytics and prediction modelling, and expand our platform to include all studies relevant to stroke research. If successful, our project would provide a field-wide research tool in stroke, as well as enabling us to create the methods that make scaling across all medical disciplines possible.