Time-dependent prediction and evaluation of variable importance using superlearning in high-dimensional clinical data Academic Article uri icon

Overview

MeSH Major

  • Algorithms
  • Artificial Intelligence
  • Blood Transfusion
  • Hemorrhage
  • Survival Analysis
  • Trauma Centers
  • Wounds and Injuries

abstract

  • The SL technique for prediction of outcome from a complex dynamic multivariate data set is superior at each time interval to standard models. In addition, the SL VIM at each time point provides insight into the time-specific drivers of future outcome, patient trajectory, and targets for clinical intervention. Thus, this automated approach mimics clinical practice, changing form and content through time to optimize the accuracy of the prognosis based on the evolving trajectory of the patient.

publication date

  • July 26, 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3744063

Digital Object Identifier (DOI)

  • 10.1097/TA.0b013e3182914553

PubMed ID

  • 23778512

Additional Document Info

start page

  • S53

end page

  • 60

volume

  • 75

number

  • 1 SUPPL1