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

文本挖掘管道加速循证医学的系统审查

• 批准号: 8325177
• 负责人: AARON M. COHEN
• 金额: $ 51.79万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-30 至 2014-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8325177
• 关键词: Affect; Area; Caring; Characteristics; Classification; Clinical; Clinical Trials; Collection; Communities; Data; Data Set; Data Sources; Databases; Deposition; Editorial Policies; Evidence Based Medicine; Evolution; Gold; Gray unit of radiation dose; Guidelines; Individual; Information Retrieval Systems; Information Services; Lead; Learning; Literature; MEDLINE; Manuals; Medical; Meta-Analysis; Metadata; Modeling; Output; Peer Review; Performance; Policy Maker; Practice Guidelines; Principal Investigator; Probability; Process; PubMed; Publishing; Randomized Controlled Trials; Recording of previous events; Reporting; Research; Research Support; Review Literature; Savings; Source; Specific qualifier value; System; Testing; Text; Time; Training; Validation; Work; Writing; abstracting; base; clinical care; control trial; cost effective; improved; indexing; performance tests; programs; systematic review; text searching; web site

DESCRIPTION (provided by applicant): The Text Mining Pipeline to Accelerate Systematic Reviews in Evidence-Based Medicine will combine important research in several areas of biomedical text mining that are necessary to enable much-needed improvements in the process of conducting systematic reviews via a text mining enhanced workflow. Our consortium will undertake three specific aims to support this work: Aim 1. Study how to create a metasearch engine and database that collects information from important systematic review sources, indexes this information consistently, and provides a robust information retrieval system with high recall and precision for accessing this expanded literature collection. Aim 2. Study how to create a literature classification and ranking system that is customizable and trainable for each user, systematic review group, and systematic review topic. This supervised learning based classification and ranking system takes as input the list of retrieved articles corresponding to a given query, and outputs them grouped by article type, in order of predicted probability of relevance to an individual writing a systematic review on the given topic. Aim 3. Study how to create a study aggregator that collects together articles that refer to the same underlying clinical trial. This will save reviewers work and time as they will now have automated assistance in determining whether two articles are independent data sources, or derive their evidence from the same primary data. Taken together, these results will inform construction of a text mining pipeline system that will decrease the manual burden of systematic reviewers during the literature collection and review process, and increase the proportion of reviewer time spent synthesizing evidence and performing meta-analyses. The system will lead to a real difference in the rate that high-quality evidence reports can be compiled. Ultimately, the coverage, dissemination, and acceptance of evidence- based medicine in the biomedical community will increase, resulting in better and more cost- effective clinical care.

描述（由申请人提供）：加速循证医学系统评价的文本挖掘管道将联合收割机结合生物医学文本挖掘的几个领域的重要研究，这些研究对于通过文本挖掘增强的工作流程在进行系统评价的过程中实现急需的改进是必要的。我们的联合体将实现三个具体目标来支持这项工作： 目标1。研究如何创建一个元数据引擎和数据库，从重要的系统性综述来源收集信息，对这些信息进行一致的索引，并提供一个强大的信息检索系统，具有高召回率和精确度，用于访问这个扩展的文献库。 目标2.研究如何创建一个文献分类和排名系统，该系统可针对每个用户、系统综述组和系统综述主题进行定制和培训。这种基于监督学习的分类和排名系统将与给定查询相对应的检索到的文章的列表作为输入，并将它们按文章类型分组输出，以预测与针对给定主题撰写系统综述的个人相关的概率。 目标3。学习如何创建一个研究聚合器，将引用相同基础临床试验的文章收集在一起。这将节省评审员的工作和时间，因为他们现在可以自动帮助确定两篇文章是否是独立的数据源，或者从相同的原始数据中获得证据。 总之，这些结果将为构建文本挖掘管道系统提供信息，该系统将减少系统评价者在文献收集和审查过程中的手动负担，并增加评价者花费在合成证据和进行荟萃分析上的时间比例。该系统将导致高质量证据报告的编制率出现真实的差异。最终，循证医学在生物医学界的覆盖率、传播和接受程度将会增加，从而产生更好、更具成本效益的临床护理。