Show simple item record

dc.contributor.authorAdams, Clive E.
dc.date.accessioned2017-10-30T14:18:18Z
dc.date.available2017-10-30T14:18:18Z
dc.date.issued2015
dc.identifier.citationShao, W., Adams, C. E., Cohen, A. M., Davis, J. M., McDonagh, M. S., Thakurta, S., Yu, P. S. & Smalheiser, N. R. (2015). Aggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trial. Methods, 74, pp.65-70.en
dc.identifier.other10.1016/j.ymeth.2014.11.006
dc.identifier.urihttp://hdl.handle.net/20.500.12904/10914
dc.description.abstractOBJECTIVEIt 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.METHODSWe 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.RESULTSArticle 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.DISCUSSIONAlthough 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.en
dc.description.urihttp://www.sciencedirect.com/science/article/pii/S1046202314003661
dc.subjectData collectionen
dc.subjectInformation storage and retrievalen
dc.titleAggregator: a machine learning approach to identifying MEDLINE articles that derive from the same underlying clinical trialen
dc.typeArticle


This item appears in the following Collection(s)

Show simple item record