Semi-automated screening of biomedical citations for systematic reviews.
Semi-automated screening of biomedical citations for systematic reviews.
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DOI：
10.1186/1471-2105-11-55
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发表时间：
2010-01-26
影响因子：
3
通讯作者：
Schmid CH
中科院分区：
文献类型：
--
作者：
Wallace BC;Trikalinos TA;Lau J;Brodley C;Schmid CH
系统性综述通过对相关文献的无偏倚评估和分析来解决特定的临床问题。引文筛选是系统评价中耗时且关键的一步。通常情况下，评审员必须评估数千篇引文，以确定符合特定评审条件的文章。我们探讨了机器学习技术在半自动引文筛选中的应用，从而减少了审稿人的工作量。我们提出了一种新的在线分类策略的引文筛选自动区分“相关”从“不相关”的引文。我们使用在不同特征空间（例如，摘要和标题文本），并由审阅者交互式地训练。半自动化的引文筛选过程是困难的，因为任何这样的策略都必须识别出所有符合系统评价条件的引文。由于类别的不平衡，这一要求变得更加困难;对于任何给定的系统综述，“相关”的引文远远少于“不相关”的引文。为了应对这些挑战，我们采用了专门为不平衡数据集开发的自定义主动学习策略。此外，我们介绍了一种新的欠采样技术。我们提供了三个真实世界系统综述数据集的实验结果，并证明我们的算法能够将其中两个数据集必须手动筛选的引文数量减少近一半，第三个数据集减少约40%，而不排除任何符合系统综述条件的引文。我们已经开发了一种用于系统综述的半自动引文筛选算法，该算法有可能大幅减少审稿人必须手动筛选的引文数量，而不会影响综述的质量和全面性。
Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and critical step in systematic reviews. Typically, reviewers must evaluate thousands of citations to identify articles eligible for a given review. We explore the application of machine learning techniques to semi-automate citation screening, thereby reducing the reviewers' workload. We present a novel online classification strategy for citation screening to automatically discriminate "relevant" from "irrelevant" citations. We use an ensemble of Support Vector Machines (SVMs) built over different feature-spaces (e.g., abstract and title text), and trained interactively by the reviewer(s). Semi-automating the citation screening process is difficult because any such strategy must identify all citations eligible for the systematic review. This requirement is made harder still due to class imbalance; there are far fewer "relevant" than "irrelevant" citations for any given systematic review. To address these challenges we employ a custom active-learning strategy developed specifically for imbalanced datasets. Further, we introduce a novel undersampling technique. We provide experimental results over three real-world systematic review datasets, and demonstrate that our algorithm is able to reduce the number of citations that must be screened manually by nearly half in two of these, and by around 40% in the third, without excluding any of the citations eligible for the systematic review. We have developed a semi-automated citation screening algorithm for systematic reviews that has the potential to substantially reduce the number of citations reviewers have to manually screen, without compromising the quality and comprehensiveness of the review.
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DOI：
10.1197/jamia.m1641
发表时间：
2005-03-01
影响因子：
6.4
作者：
Aphinyanaphongs, Y;Tsamardinos, I;Aliferis, CF
通讯作者：
Aliferis, CF
影响因子：
4.4
作者：
Barza, Michael;Trikalinos, Thomas A.;Lau, Joseph
通讯作者：
Lau, Joseph
DOI：
10.1197/jamia.m2996
发表时间：
2009-01-01
影响因子：
6.4
作者：
Kilicoglu, Halil;Demner-Fushman, Dina;Haynes, R. Brian
通讯作者：
Haynes, R. Brian
DOI：
10.1007/978-0-387-09823-4_45
发表时间：
2010-01-01
期刊：
DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION
影响因子：
--
作者：
Chawla, Nitesh V.
通讯作者：
Chawla, Nitesh V.
影响因子：
39.2
作者：
Counsell, C
通讯作者：
Counsell, C