DenseReviewer：基于稠密检索的系统综述筛选优先级排序工具
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
AI总结：
本文提出DenseReviewer工具，基于稠密检索与审稿人反馈对医学系统综述中的研究进行优先级排序，并提供Web筛选工具和Python库以支持高效筛选与新主动学习方法开发。
摘要：
筛选是医学系统综述中一项耗时且劳动密集但又必不可少的任务，因为通常需要筛选数以万计的研究。对相关研究进行优先级排序，使下游系统综述创建任务能够更早开始并节省时间。在先前的工作中，我们开发了一种稠密检索方法，在标题和摘要筛选阶段利用审稿人反馈对相关研究进行优先级排序。我们的方法在有效性和效率方面均优于先前的主动学习方法。在本演示中，我们通过创建以下内容扩展了先前的工作：（1）一个基于Web的筛选工具，使最终用户能够利用最先进的方法筛选研究；（2）一个Python库，集成了模型和反馈机制，并允许研究人员开发和展示新的主动学习方法。我们描述了该工具的设计，并展示了它如何辅助筛选。该工具可在 https://densereviewer.ielab.io 获取。源代码也在 https://github.com/ielab/densereviewer 开源。
英文摘要：
Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevant studies to be screened allows downstream systematic review creation tasks to start earlier and save time. In previous work, we developed a dense retrieval method to prioritise relevant studies with reviewer feedback during the title and abstract screening stage. Our method outperforms previous active learning methods in both effectiveness and efficiency. In this demo, we extend this prior work by creating (1) a web-based screening tool that enables end-users to screen studies exploiting state-of-the-art methods and (2) a Python library that integrates models and feedback mechanisms and allows researchers to develop and demonstrate new active learning methods. We describe the tool's design and showcase how it can aid screening. The tool is available at https://densereviewer.ielab.io. The source code is also open sourced at https://github.com/ielab/densereviewer.
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。
