• Corpus ID: 219179781

Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model

@article{Schuler2020PerformanceMF,
  title={Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model},
  author={Alejandro Schuler and Aashish Bhardwaj and Vincent X. Liu},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.01752}
}
Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices. However, if these models are used to allocate interventions to patients, standard metrics calculated from retrospective data are only related to model utility (in terms of reductions in outcomes) under certain assumptions. When predictions are delivered repeatedly throughout time (e.g. in a patient encounter), the relationship between standard metrics… 

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