A new algorithm for reducing the workload of experts in performing systematic reviews
A new algorithm for reducing the workload of experts in performing systematic reviews
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DOI：
10.1136/jamia.2010.004325
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发表时间：
2010-07-01
影响因子：
6.4
通讯作者：
O'Blenis, Peter
中科院分区：
文献类型：
--
作者：
Matwin, Stan;Kouznetsov, Alexandre;O'Blenis, Peter
目的为了确定是否因子化版本的补充朴素贝叶斯（FCNB）分类器可以减少专家审查期刊文章纳入疾病treatment.Design的药物类疗效的系统评价所花费的时间建议的分类器进行了评估，建立从15个系统的药物类综述在以前的工作中使用的测试集合。构建FCNB分类器，将每篇文章分类为包含高质量的药物类别特定证据或不包含高质量的药物类别特定证据。增加了权重工程（WE）技术，以减少对基于医学主题词（MeSH）和基于出版物类型（PubType）的特征的低估。结果采用FCNB/WE分类器对一篇系统综述的最小工作量减少率为8.5%;最高为62.2%，15个主题的平均数为33.5%。这是15.0%，高于平均工作量减少使用投票感知器为基础的自动引文classificationsystem.Conclusion的FCNB/WE分类是简单的，易于实现，并产生显着更好的结果，在减少工作量比以前实现。结果支持它是一种有用的算法，用于基于机器学习的疾病治疗药物类疗效系统评价的自动化。
Objective To determine whether a factorized version of the complement naive Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment.Design The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifier's parameters and performance.Measurements Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance.Results The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system.Conclusion The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning- based automation of systematic reviews of drug class efficacy for disease treatment.