Journal of Undergraduate Research
Keywords
ADAMTS13 deficiency, prediction algorithm, TTP, thrombotic thrombocytopenic purpura
College
Life Sciences
Department
Biology
Abstract
Despite previous work by our group and other researchers, the existing methods to predict ADAMTS13 deficiency are inadequate. Through the use of machine learning techniques applied to mined patient data we have improved the accuracy and efficiency of our existing ADAMTS13 deficiency prediction algorithm. In conjunction with ARUP Laboratories, who pioneered the original work, we have continued to improve the algorithm accuracy. Significant correlation for severe ADAMTS13 deficiency was seen for four of the observed variables: indirect bilirubin, reticulocyte percentage, creatinine, and platelet count; a fifth variable, D-dimer, just missed significance but performed well.
Recommended Citation
Nevers, Steven; Brandt, David; and Biggs, Matthew
(2014)
"A Multi-Faceted Approach to Improving ADAMTS13 Deficiency Prediction Algorithm,"
Journal of Undergraduate Research: Vol. 2014:
Iss.
1, Article 721.
Available at:
https://scholarsarchive.byu.edu/jur/vol2014/iss1/721