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

ABSTRACT: 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.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.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.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.

SUBMITTER: Shao W

PROVIDER: S-EPMC4339517 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Shao Weixiang W Adams Clive E CE Cohen Aaron M AM Davis John M JM McDonagh Marian S MS Thakurta Sujata S Yu Philip S PS Smalheiser Neil R NR

Methods (San Diego, Calif.) 20141120

<h4>Objective</h4>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.<h4>Methods</h4>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 s ...[more]

PMID: 25461812

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Project description:OBJECTIVES:Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. DESIGN:We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. SETTING:Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members. PATIENTS:ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. CONCLUSIONS:A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.

Dataset Information

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

Publications

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

Similar Datasets

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