Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide.

ABSTRACT: 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

SUBMITTER: Marshall IJ 

PROVIDER: S-EPMC6030513 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature