Comparison of variable selection methods for clinical predictive modeling.

Journal: International journal of medical informatics
Published Date:

Abstract

OBJECTIVE: Modern machine learning-based modeling methods are increasingly applied to clinical problems. One such application is in variable selection methods for predictive modeling. However, there is limited research comparing the performance of classic and modern for variable selection in clinical datasets.

Authors

  • L Nelson Sanchez-Pinto
    Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  • Laura Ruth Venable
    Rollins School of Public Health, Emory University, Atlanta, GA, USA.
  • John Fahrenbach
    The Center for Healthcare Delivery Science and Innovation, The University of Chicago, Chicago, IL, USA.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.