Login / Signup

Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation.

Cynthia A JackeviciusJaeJin AnDennis T KoJoseph S RossSuveen AngraalJoshua D WallachMaria KohJeeeun SongHarlan M Krumholz
Published in: BMJ open (2019)
Despite use of the same data source, a diversity of approaches, methods and results was produced by the 29 SPRINT Challenge competition submissions for clinical risk prediction. Of the nine evaluable risk prediction tools, clinical performance was suboptimal. By collating an overview of the range of approaches taken, researchers may further optimise the development of risk prediction tools in SPRINT-eligible populations, and our findings may inform the conduct of future similar open science projects.
Keyphrases
  • data analysis
  • high intensity
  • public health
  • resistance training
  • electronic health record
  • machine learning
  • body composition
  • current status
  • big data
  • artificial intelligence