Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial

Weixiang Shao, Clive E. Adams, Aaron Cohen, John M. Davis, Marian McDonagh, Sujata Thakurta, Philip S. Yu, Neil R. Smalheiser

Research output: Contribution to journalArticle

6 Scopus citations

Abstract

OBJECTIVE: It is important to identify separate publications that report outcomes from the same underlying clinical trial, in order to avoid over-counting these as independent pieces of evidence.

METHODS: We created positive and negative training sets (comprised of pairs of articles reporting on the same condition and intervention) that were, or were not, linked to the same clinicaltrials.gov trial registry number. Features were extracted from MEDLINE and PubMed metadata; pairwise similarity scores were modeled using logistic regression.

RESULTS: Article pairs from the same trial were identified with high accuracy (F1 score=0.843). We also created a clustering tool, Aggregator, that takes as input a PubMed user query for RCTs on a given topic, and returns article clusters predicted to arise from the same clinical trial.

DISCUSSION: Although painstaking examination of full-text may be needed to be conclusive, metadata are surprisingly accurate in predicting when two articles derive from the same underlying clinical trial.

Original languageEnglish (US)
Pages (from-to)65-70
Number of pages6
JournalMethods (San Diego, Calif.)
Volume74
DOIs
StatePublished - Mar 1 2015

Keywords

  • Bias
  • Clinical trials
  • Evidence-based medicine
  • Informatics
  • Information retrieval
  • Systematic reviews

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry, Genetics and Molecular Biology(all)

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