title: Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60612, USA.
authors.forename: Weixiang
authors.initials: W
authors.lastname: Shao
authors.affiliation: Division of Psychiatry, University of Nottingham, Nottingham, UK.
authors.forename: Clive E
authors.initials: CE
authors.lastname: Adams
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Aaron M
authors.initials: AM
authors.lastname: Cohen
authors.affiliation: Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA.
authors.forename: John M
authors.initials: JM
authors.lastname: Davis
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Marian S
authors.initials: MS
authors.lastname: McDonagh
authors.affiliation: Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
authors.forename: Sujata
authors.initials: S
authors.lastname: Thakurta
authors.affiliation: Department of Computer Science, University of Illinois at Chicago, Chicago, IL 60612, USA.
authors.forename: Philip S
authors.initials: PS
authors.lastname: Yu
authors.affiliation: Department of Psychiatry, University of Illinois at Chicago, Chicago, IL 60612, USA. Electronic address: neils@uic.edu.
authors.forename: Neil R
authors.initials: NR
authors.lastname: Smalheiser
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D006801:Humans; D016239:MEDLINE / Q000706:statistics & numerical data*; D000069550:Machine Learning
keywords: Bias; Clinical trials; Evidence-based medicine; Informatics; Information retrieval; Systematic reviews