Predicting outcomes in patients with aortic stenosis using machine learning: the aortic stenosis risk (asterisk) score

HIGHLIGHTS

• who: Romain Capoulade u200d u200d and collaborators from the Bootstrapping was done to obtain statistical measures of performanceA total of , stratified bootstrap splits (80% training, % testing) were performed for evaluation, and the results are reported over the bootstrapped test sets. We used AUC analysis as well as the, year HRs. For the HRs, we chose the upper quartile of risk to denote the high-u00adrisk subgroup. Cox proportional hazards models were used for time-u00adto-u00adevent analyses. CIs for the AUCs were calculated by meanu00b1, SEs of the AUCs across the , bootstrap splits. All CIs in the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.