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2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (2015)
Miami, FL, USA
Oct. 15, 2015 to Oct. 17, 2015
ISBN: 978-1-4673-9662-2
pp: 1-2
Yuanyuan Feng , University of Maryland Baltimore County, Baltimore, USA
Vandana P. Janeja , University of Maryland Baltimore County, Baltimore, USA
Yelena Yesha , University of Maryland Baltimore County, Baltimore, USA
Napthali Rishe , Florida International University, USA
Michael A. Grasso , University of Maryland School of Medicine, Baltimore, USA
Amanda Niskar , University of Maryland School of Medicine, Baltimore, USA
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.
INDEX TERMS
outcome prediction, data mining, clinical trials metadata, rheumatoid arthritis
CITATION

Yuanyuan Feng, V. P. Janeja, Y. Yesha, N. Rishe, M. A. Grasso and A. Niskar, "Poster: Classifying primary outcomes in rheumatoid arthritis: Knowledge discovery from clinical trial metadata," 2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), Miami, FL, USA, 2015, pp. 1-2.
doi:10.1109/ICCABS.2015.7344722
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