Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews

This study developed, calibrated, and evaluated a machine learning classifier designed to reduce study identification workload in Cochrane for producing systematic reviews. A machine learning classifier for retrieving randomized controlled trials (RCTs) was developed (the “Cochrane RCT Classifier”),...

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Bibliographic Details
Published in:	Journal of clinical epidemiology Vol. 133; pp. 140 - 151
Main Authors:	Thomas, James, McDonald, Steve, Noel-Storr, Anna, Shemilt, Ian, Elliott, Julian, Mavergames, Chris, Marshall, Iain J.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.05.2021 Elsevier Limited Elsevier
Subjects:	Algorithms Automation Bibliographic data bases Calibration Classification Classifiers Clinical trials Cochrane Library Confidence intervals Cost control Crowdsourcing Databases, Bibliographic - standards Databases, Bibliographic - statistics & numerical data Datasets Efficiency Epidemiology Evaluation Humans Information retrieval Information Storage and Retrieval - methods Information Storage and Retrieval - standards Information Storage and Retrieval - statistics & numerical data Internal Medicine Learning algorithms Machine Learning Methods/methodology Original Production methods Randomized controlled trials Randomized Controlled Trials as Topic - classification Randomized Controlled Trials as Topic - methods Randomized Controlled Trials as Topic - standards Randomized Controlled Trials as Topic - statistics & numerical data Recall Searching Study classifiers Systematic review Systematic reviews Systematic Reviews as Topic - methods Systematic Reviews as Topic - standards Validity Workflow Workload Workload - statistics & numerical data Workloads Crowdsourcing Methods/methodology Automation Searching Cochrane Library Machine learning Systematic reviews Study classifiers Information retrieval Randomized controlled trials
ISSN:	0895-4356, 1878-5921, 1878-5921
Online Access:	Get full text
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