Literature DB >> 29677940

Extracting the Population, Intervention, Comparison and Sentiment from Randomized Controlled Trials.

Markus Zlabinger1, Linda Andersson1, Jon Brassey2, Allan Hanbury1.   

Abstract

In this paper, an identification approach for the Population (e.g. patients with headache), the Intervention (e.g. aspirin) and the Comparison (e.g. vitamin C) in Randomized Controlled Trials (RCTs) is proposed. Contrary to previous approaches, the identification is done on a word level, rather than on a sentence level. Additionally, we classify the sentiment of RCTs to determine whether an Intervention is more effective than its Comparison. Two new corpora were created to evaluate both approaches. In the experiments, an average F1 score of 0.85 for the PIC identification and 0.72 for the sentiment classification was achieved.

Entities:  

Keywords:  Information extraction; machine learning; natural language processing; sentiment analysis

Mesh:

Year:  2018        PMID: 29677940

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  1 in total

1.  Validating GAN-BioBERT: A Methodology for Assessing Reporting Trends in Clinical Trials.

Authors:  Joshua J Myszewski; Emily Klossowski; Patrick Meyer; Kristin Bevil; Lisa Klesius; Kristopher M Schroeder
Journal:  Front Digit Health       Date:  2022-05-24
  1 in total

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