Studying the potential impact of automated document classification on scheduling a systematic review update.
Studying the potential impact of automated document classification on scheduling a systematic review update.
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DOI：
10.1186/1472-6947-12-33
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发表时间：
2012-04-19
影响因子：
3.5
通讯作者：
McDonagh M
中科院分区：
文献类型：
--
作者：
Cohen AM;Ambert K;McDonagh M
系统评价(SRS)是循证医学的重要组成部分，为临床实践和广泛的医学主题政策提供支持。然而，生产SRS是资源密集型的，他们审查的研究进展导致SRS变得过时，需要更新。虽然已经研究了如何以及何时更新SRS的问题，但确定何时更新的最佳方法仍然不清楚，需要进一步研究。在这项工作中，我们研究了一个基于机器学习的自动化系统的潜在影响，该系统用于在SR主题中有新的出版物可用时提供警报。其中一些新出版物尤其重要，因为它们报告的调查结果更有可能启动审查更新。为此，我们设计了一个分类算法来识别可能包含在SR更新中的文章，以及一个旨在识别主题领域中最重要的出版物的注释方案。使用包含70,000多篇文章的SR数据库，我们对研究期间收到更新的9个主题的文章进行了注释。然后，根据符合主题纳入标准的出版物的总体正确和不正确警报率，以及其识别主题领域中重要的、促使更新的出版物的能力，对该算法进行了评估。我们最初的方法基于我们之前在主题特定SR出版物分类方面的工作，识别了超过70%的最重要的新出版物，同时保持了较低的总体警戒率。我们初步分析了使用基于信息学的机器学习方法来帮助SR更新规划过程中的机会和挑战。警报可能是规划、调度和分配SR更新资源的有用工具，可改进需要SR的大量医学主题的及时性和覆盖面。虽然这种初始方法的性能并不完美，但它可能是对当前调度SR更新的方法的有用补充。专门针对这项工作所确定的重要出版物类型的办法可能会改善结果。
Systematic Reviews (SRs) are an essential part of evidence-based medicine, providing support for clinical practice and policy on a wide range of medical topics. However, producing SRs is resource-intensive, and progress in the research they review leads to SRs becoming outdated, requiring updates. Although the question of how and when to update SRs has been studied, the best method for determining when to update is still unclear, necessitating further research. In this work we study the potential impact of a machine learning-based automated system for providing alerts when new publications become available within an SR topic. Some of these new publications are especially important, as they report findings that are more likely to initiate a review update. To this end, we have designed a classification algorithm to identify articles that are likely to be included in an SR update, along with an annotation scheme designed to identify the most important publications in a topic area. Using an SR database containing over 70,000 articles, we annotated articles from 9 topics that had received an update during the study period. The algorithm was then evaluated in terms of the overall correct and incorrect alert rate for publications meeting the topic inclusion criteria, as well as in terms of its ability to identify important, update-motivating publications in a topic area. Our initial approach, based on our previous work in topic-specific SR publication classification, identifies over 70% of the most important new publications, while maintaining a low overall alert rate. We performed an initial analysis of the opportunities and challenges in aiding the SR update planning process with an informatics-based machine learning approach. Alerts could be a useful tool in the planning, scheduling, and allocation of resources for SR updates, providing an improvement in timeliness and coverage for the large number of medical topics needing SRs. While the performance of this initial method is not perfect, it could be a useful supplement to current approaches to scheduling an SR update. Approaches specifically targeting the types of important publications identified by this work are likely to improve results.
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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
影响因子：
39.2
作者：
Shojania, Kaveh G.;Sampson, Margaret;Moher, David
通讯作者：
Moher, David
影响因子：
7.2
作者：
Sampson, Margaret;Shojania, Kaveh G.;Moher, David
通讯作者：
Moher, David
DOI：
10.1136/jamia.2010.004325
发表时间：
2010-07-01
影响因子：
6.4
作者：
Matwin, Stan;Kouznetsov, Alexandre;O'Blenis, Peter
通讯作者：
O'Blenis, Peter