Research Synthesis Methods

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

Journal Article

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.

Related Topics

Related Publications

Related Content

Site Footer


This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.