Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.
Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.
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DOI：
10.1002/jrsm.1287
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发表时间：
2018-12
影响因子：
9.8
通讯作者：
Wallace BC
中科院分区：
文献类型：
--
作者：
Marshall IJ;Noel-Storr A;Kuiper J;Thomas J;Wallace BC
Machine learning (ML) algorithms have proven highly accurate for identifying Randomized Controlled Trials (RCTs) but are not used much in practice, in part because the best way to make use of the technology in a typical workflow is unclear. In this work, we evaluate ML models for RCT classification (support vector machines, convolutional neural networks, and ensemble approaches). We trained and optimized support vector machine and convolutional neural network models on the titles and abstracts of the Cochrane Crowd RCT set. We evaluated the models on an external dataset (Clinical Hedges), allowing direct comparison with traditional database search filters. We estimated area under receiver operating characteristics (AUROC) using the Clinical Hedges dataset. We demonstrate that ML approaches better discriminate between RCTs and non‐RCTs than widely used traditional database search filters at all sensitivity levels; our best‐performing model also achieved the best results to date for ML in this task (AUROC 0.987, 95% CI, 0.984‐0.989). We provide practical guidance on the role of ML in (1) systematic reviews (high‐sensitivity strategies) and (2) rapid reviews and clinical question answering (high‐precision strategies) together with recommended probability cutoffs for each use case. Finally, we provide open‐source software to enable these approaches to be used in practice.
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影响因子：
3.8
作者：
McKibbon, Kathleen Ann;Wilczynski, Nancy Lou;Haynes, Robert Brian
通讯作者：
Haynes, Robert Brian
影响因子：
120.7
作者：
Begg, C;Cho, M;Stroup, DF
通讯作者：
Stroup, DF
影响因子：
3.5
作者：
Wilczynski, Nancy L;Morgan, Douglas;Haynes, R Brian
通讯作者：
Haynes, R Brian
DOI：
10.1007/3-540-45014-9_1
发表时间：
2000-01-01
期刊：
MULTIPLE CLASSIFIER SYSTEMS
影响因子：
--
作者：
Dietterich, TG
通讯作者：
Dietterich, TG
DOI：
10.1093/jamia/ocu025
发表时间：
2015-05
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Cohen AM;Smalheiser NR;McDonagh MS;Yu C;Adams CE;Davis JM;Yu PS
通讯作者：
Yu PS