Making machine learning matter to clinicians: model actionability in medical decision-making

HIGHLIGHTS

• who: Daniel E. Ehrmann from the Published in partnership with Seoul National University Bundang Hospital have published the research work: Making machine learning matter to clinicians: model actionability in medical decision-making, in the Journal: (JOURNAL) of 13/01/2023
• what: More broadly, the authors seek to expand the available tools that holistically evaluate a model`s potential clinical impacts. The authors propose a metric that measures a model`s ability to potentially augment medical decision-making by reducing uncertainty in specific clinical scenarios. The authors propose that clinicians may value entropy reduction, and the . . .
  

   

  

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