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Trialstreamer: a living, automatically updated database of clinical trial reports

Research output: Contribution to journalArticle

Iain Marshall, Benjamin Nye, Joël Kuiper, Anna Noel-Storr, Rachel Marshall, Rory Maclean, Ani Nenkova, James Thomas, Byron C Wallace

Original languageEnglish
JournalJournal of the American Medical Informatics Association : JAMIA
Publication statusAccepted/In press - 28 Jun 2020


King's Authors


Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in healthcare, but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports.
Materials and Methods
Trialstreamer, continuously monitors PubMed and the WHO International Clinical Trials Registry Platform (ICTRP), looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial populations, interventions and outcomes (the ‘PICO’) and map these snippets to normalised MeSH vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies.
As of early June 2020, we have indexed 673,191 publications of RCTs, of which 22,363 were published in the first five months of 2020 (142/day). We additionally include 304,111 trial registrations from ICTRP. The median trial sample size was 66.
We present an automated system for finding and categorising RCTs. This yields a novel resource: A database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (

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