一个用于系统评价中引文收集和筛选自动化的开源集成框架
An open-source integrated framework for the automation of citation collection and screening in systematic reviews
摘要：
科学生产的指数增长使得二次文献删节的要求越来越高。我们引入了一个新的系统性综述开源框架，大大减少了收集和筛选科学文献的时间和工作量。该框架提供了三个主要工具：1）自动引文搜索引擎和管理器，它使用统一的查询语法从多个在线来源收集记录，2）贝叶斯，主动机器学习，基于迭代人机交互的引文筛选工具，以提高预测准确性，以及3）半自动，数据驱动的查询生成器，用于从现有的引文数据集创建新的搜索查询。为了评估自动筛选器的性能，我们使用贝叶斯模拟来估计中值后验灵敏度和效率[90%可信区间]，以预测未检测到的潜在相关记录的分布。在一个示例主题上进行测试，该框架通过引文管理器收集了17，755条独特的记录; 766条记录需要人工评估，其余记录被自动分类器排除;理论效率为95.6% [95.3%，95.7%]，灵敏度为100% [93.5%，100%]。从标记数据集生成新的搜索查询，并收集了82，579条额外记录;只有567条记录在自动筛选后需要人工审查，并发现了6个额外的阳性匹配。总体预期灵敏度降至97.3% [73.8%，100%]，而效率提高至98.6% [98.2%，98.7%]。该框架可以通过简化引文收集和筛选，同时表现出非凡的敏感性，大大减少进行大型文献综述所需的工作量。这样的工具可以提高系统评价的标准化和可重复性。
英文摘要：
The exponential growth of scientific production makes secondary literature abridgements increasingly demanding. We introduce a new open-source framework for systematic reviews that significantly reduces time and workload for collecting and screening scientific literature. The framework provides three main tools: 1) an automatic citation search engine and manager that collects records from multiple online sources with a unified query syntax, 2) a Bayesian, active machine learning, citation screening tool based on iterative human-machine interaction to increase predictive accuracy and, 3) a semi-automatic, data-driven query generator to create new search queries from existing citation data sets. To evaluate the automatic screener's performance, we estimated the median posterior sensitivity and efficiency [90% Credible Intervals] using Bayesian simulation to predict the distribution of undetected potentially relevant records. Tested on an example topic, the framework collected 17,755 unique records through the citation manager; 766 records required human evaluation while the rest were excluded by the automatic classifier; the theoretical efficiency was 95.6% [95.3%, 95.7%] with a sensitivity of 100% [93.5%, 100%]. A new search query was generated from the labelled dataset, and 82,579 additional records were collected; only 567 records required human review after automatic screening, and six additional positive matches were found. The overall expected sensitivity decreased to 97.3% [73.8%, 100%] while the efficiency increased to 98.6% [98.2%, 98.7%]. The framework can significantly reduce the workload required to conduct large literature reviews by simplifying citation collection and screening while demonstrating exceptional sensitivity. Such a tool can improve the standardization and repeatability of systematic reviews.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
