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

用于急性缺血性中风患者治疗结果的综合数据驱动平台

• 批准号: 10081942
• 负责人: Kevin Kallmes
• 金额: $ 19.62万
• 依托单位: NESTED KNOWLEDGE, INC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2021-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10081942
• 关键词: Acute; Adoption; Agreement; American Association for the Advancement of Science; Anesthesia procedures; Automation; Biomedical Research; Caring; Catheters; Client; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Sciences; Collaborations; Communication; Computer software; Data; Data Analyses; Data Analytics; Data Set; Decision Making; Devices; Discipline; Engineering; Ensure; Foundations; Funding; Future; Grant; Growth; Hour; Imaging Device; Informatics; Information Systems; Insurance; Intracranial Aneurysm; Ischemic Stroke; Knowledge; Literature; Mechanics; Medical; Medicine; Meta-Analysis; Methodology; Methods; Mission; National Institute of Neurological Disorders and Stroke; Neuroprotective Agents; Outcome; Patient-Focused Outcomes; Patients; Pharmacotherapy; Phase; Physicians; Predictive Analytics; Process; Production; Productivity; Publications; Publishing; Reading; Reporting; Research; Research Personnel; Review Literature; Science; Small Business Innovation Research Grant; Stents; Stroke; Technology; Testing; Thrombectomy; United States National Academy of Sciences; United States National Institutes of Health; Update; Validation; Vision; Visual; Visualization; Work; adjudicate; arm; base; data harmonization; data management; data visualization; design; evidence base; experience; indexing; knowledge base; new technology; predictive modeling; tool; working group

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 However, current literature review methods are outdated, unstructured, and disorganized, and potentially lifesaving data are accessible and comparable only with hundreds of hours of work in literature reviewing.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.

项目摘要 从报销到监管清除到身体和患者的决策 - 制作，取决于临床出版物。临床出版物是基于证据的主要机制 因此，决策和文献综述是主要的全面临床结果比较 方法论。但是，当前的文献综述方法已过时，无组织和混乱，并且 潜在的救生数据是可以访问的，并且仅与文献中数百小时的工作相提并论 评论。特别是考虑到技术驱动数据管理的增长，当前的范式 将临床结果数据从根本上结合在一起，无法与一般医学受众沟通 给定治疗作品，以透明，全面和可更新的形式进行，典型的荟萃分析是 甚至无法证明其在现有指数之间的搜索水平。 从NIH的数据信息学工作组中，许多组织都认识到了这个问题 （DIWG）到AAAS到美国国家科学，工程，医学学院，这些学院都公开 指出以技术驱动的协调和数据可视化对于有效共享研究是必要的。 但是，搜索性，可视化和协调努力尚未渗透到医学出版中。 DIWG指出：“进行生物医学研究的技术和方法的巨大变化已经 将科学生产的瓶颈从数据生产转移到数据管理，通信和 数据解释。“我们同意临床科学中最大的瓶颈实际上与 沟通，我们认为这是由于不足以在出版中采用新颖的技术， 特别的交互式视觉方法是呈现范围范围的数据和数据收集的自动化方法。 我们的愿景是创建一个经过全面研究，不断更新，易于消化的平台 基于专家设计的，自动化的科学出版物中介绍的关键数据的传播 从现有出版物中提取数据。在中风中首次亮相之后 - 因为我们以前的经验 字段 - 我们已经实现了交互式，视觉荟萃分析的概念验证和部分自动数据 从PDF中提取。现在，我们建议自动化荟萃分析的基础，搜索/包容性 通过数据分析和预测建模进行过程，并将我们的平台扩展到包括所有研究 与中风研究有关。如果成功的话，我们的项目也将提供中风的范围范围内研究工具 使我们能够创建使所有医疗学科范围扩展的方法。