UMBC ebiquity

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

Authors: Yuanyuan Feng, Vandana P Janeja, Yelena Yesha, Naphtali Rishe, Michael A. Grasso, and Amanda Niskar

Book Title: 2015 IEEE 5th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS)

Date: November 30, 2015

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.

Type: InProceedings

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