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Keywords

Children, Data mining, Decision support, Distress, Methods, Parent, Procedure

 

Authors

  1. HANRAHAN, KIRSTEN ARNP, DNP
  2. McCARTHY, ANN MARIE PhD, RN
  3. KLEIBER, CHARMAINE PhD, RN
  4. ATAMAN, KAAN PhD
  5. STREET, W. NICK PhD
  6. ZIMMERMAN, M. BRIDGET PhD
  7. ERSIG, ANNE L. PhD, RN

Abstract

This secondary data analysis used data mining methods to develop predictive models of child risk for distress during a healthcare procedure. Data used came from a study that predicted factors associated with children's responses to an intravenous catheter insertion while parents provided distraction coaching. From the 255 items used in the primary study, 44 predictive items were identified through automatic feature selection and used to build support vector machine regression models. Models were validated using multiple cross-validation tests and by comparing variables identified as explanatory in the traditional versus support vector machine regression. Rule-based approaches were applied to the model outputs to identify overall risk for distress. A decision tree was then applied to evidence-based instructions for tailoring distraction to characteristics and preferences of the parent and child. The resulting decision support computer application, titled Children, Parents and Distraction, is being used in research. Future use will support practitioners in deciding the level and type of distraction intervention needed by a child undergoing a healthcare procedure.