APS: An Active PubMed Search System for Technology Assisted Reviews

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2020-07-25 DOI:10.1145/3397271.3401401

Dan Li, Panagiotis Zafeiriadis, E. Kanoulas

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引用次数: 4

Abstract

Systematic reviews constitute the cornerstone of Evidence-based Medicine. They can provide guidance to medical policy-making by synthesizing all available studies regarding a certain topic. However, conducting systematic reviews has become a laborious and time-consuming task due to the large amount and rapid growth of published literature. The TAR approaches aim to accelerate the screening stage of systematic reviews by combining machine learning algorithms and human relevance feedback. In this work, we built an online active search system for systematic reviews, named APS, by applying an state-of-the-art TAR approach -- Continuous Active Learning. The system is built on the top of the PubMed collection, which is a widely used database of biomedical literature. It allows users to conduct the abstract screening for systematic reviews. We demonstrate the effectiveness and robustness of the APS in detecting relevant literature and reducing workload for systematic reviews using the CLEF TAR 2017 benchmark.

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APS:技术辅助评论的主动PubMed搜索系统

系统评价是循证医学的基石。他们可以通过综合有关某一主题的所有现有研究，为医疗决策提供指导。然而，由于已发表的文献数量庞大且增长迅速，进行系统评价已成为一项费力且耗时的任务。TAR方法旨在通过结合机器学习算法和人类相关性反馈来加速系统评论的筛选阶段。在这项工作中，我们通过应用最先进的TAR方法——持续主动学习，为系统评论建立了一个在线主动搜索系统，名为APS。该系统建立在PubMed collection的基础上，PubMed collection是一个广泛使用的生物医学文献数据库。它允许用户进行系统审查的抽象筛选。我们使用CLEF TAR 2017基准证明了APS在检测相关文献和减少系统评价工作量方面的有效性和稳健性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

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