Extending PubMed searches to ClinicalTrials.gov through a machine learning approach for systematic reviews.

Journal: Journal of clinical epidemiology
Published Date: Jul 5, 2018

Abstract

OBJECTIVES: Despite their essential role in collecting and organizing published medical literature, indexed search engines are unable to cover all relevant knowledge. Hence, current literature recommends the inclusion of clinical trial registries in systematic reviews (SRs). This study aims to provide an automated approach to extend a search on PubMed to the ClinicalTrials.gov database, relying on text mining and machine learning techniques.

Authors

  • Corrado Lanera
    Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
  • Clara Minto
    Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan 18, Padova 35131, Italy.
  • Abhinav Sharma
    Department of Biological Sciences and Bioengineering (BSBE), IIT, Kanpur, India.
  • Dario Gregori
    Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
  • Paola Berchialla
    Medical Statistics Unit, Department of Clinical and Biological Sciences, University of Torino, Italy.
  • Ileana Baldi
    Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic and Vascular Sciences, University of Padova, Via Loredan 18, Padova 35131, Italy. Electronic address: ileana.baldi@unipd.it.

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