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Leveraging Advanced Analytics to Generate Dynamic Medical Systematic Reviews

机译：利用高级分析生成动态医疗系统评论

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摘要

According to Khan et al, "a review earns the adjective systematic if it is based on a clearly formulated question, identifies relevant studies, appraises their quality and summarizes the evidence by use of explicit methodology". Conducting systematic reviews tend to be resource intensive and may suffer from problems such as publication bias, time-lag bias, duplicate bias, citation bias, and outcome reporting bias. This research aims to develop a system to facilitate the creation of systematic reviews. Starting with a clinical question, the proposed system will query ClinicalTrial.gov to search published RCTs. The system will exploit advanced data analytics techniques to systematically mine clinical trials obtained from the ClinicalTrial.gov. From the theoretical perspective, the system provides context for exploring the feasibility and efficacy of using advanced analytics techniques for generating machine readable, real time medical evidence. From a practical perspective, the system is expected to produce cost efficient medical evidence.
机译：根据Khan等人的说法，“如果基于明确提出的问题，鉴定相关研究，评估其质量并通过使用明确的方法对证据进行总结，则评论可以使该形容词系统化”。进行系统的审查往往会占用大量资源，并且可能会遇到诸如出版偏见，时滞偏见，重复偏见，引文偏见和结果报告偏见等问题。这项研究旨在开发一种系统来促进系统评价的创建。从临床问题开始，提出的系统将查询ClinicalTrial.gov来搜索已发布的RCT。该系统将利用先进的数据分析技术来系统地挖掘从ClinicalTrial.gov获得的临床试验。从理论上讲，该系统为探索使用高级分析技术生成机器可读的实时医学证据的可行性和有效性提供了上下文。从实际的角度来看，该系统有望产生具有成本效益的医学证据。

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