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 However, current literature review methods are outdated, unstructured, and disorganized, and potentially lifesaving data are accessible and comparable only with hundreds of hours of work in literature reviewing.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

项目总结