Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide
By Iain James Marshall, Anna Noel-Storr, Joël Kuiper, James Thomas, Byron C. Wallace. Research Synthesis Methods, 2018
368 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/b646ba235ba49ed545c7903dd033b50e3fa99f34
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In Artificial Intelligence
- Byron C. Wallace
- Jay DeYoung
- Eric P. Lehman
- Regina Barzilay
- Sarah Wiegreffe
- Ana Marasovic
- Yoav Goldberg
- Mohit Bansal
Citations in the corpus
- Drug treatments for covid-19: living systematic review and network meta-analysis (2020, neutral): “14 We tracked preprints of randomised trials for updates and through publication: when data were discrepant, we used the most recent data.”
- Inferring Which Medical Treatments Work from Reports of Clinical Trials (2019, methodology): “(2)We used the RobotReviewer RCT classifier, which improves upon the standard MEDLINE RCT filter (Marshall et al., 2018), but some false positives remain.”