• DocumentCode
    3703447
  • Title

    Poster: Classifying primary outcomes in rheumatoid arthritis: Knowledge discovery from clinical trial metadata

  • Author

    Yuanyuan Feng;Vandana P. Janeja;Yelena Yesha;Napthali Rishe;Michael A. Grasso;Amanda Niskar

  • Author_Institution
    University of Maryland Baltimore County, Baltimore, USA
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    2
  • Abstract
    Early prediction of treatment outcomes in RA clinical trials is critical for both patient safety and trial success. We hypothesize that an approach employing metadata of clinical trials could provide accurate classification of primary outcomes before trial implementation. We retrieved RA clinical trials metadata from ClinicalTrials.gov. Four quantitative outcome measures that are frequently used in RA trials, i.e., ACR20, DAS28, and AE/SAE, were the classification targets in the model. Classification rules were applied to make the prediction and were evaluated. The results confirmed our hypothesis. We concluded that the metadata in clinical trials could be used to make early prediction of the study outcomes with acceptable accuracy.
  • Keywords
    "Clinical trials","Metadata","Arthritis","Diseases","Predictive models","Safety","Classification algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Computational Advances in Bio and Medical Sciences (ICCABS), 2015 IEEE 5th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICCABS.2015.7344722
  • Filename
    7344722