Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions

@article{Feng2022MonitoringML,
  title={Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions},
  author={Jean Feng and Alexej Gossmann and Gene A. Pennello and Nicholas A. Petrick and Berkman Sahiner and Romain Pirracchio},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.09781}
}
Monitoring the performance of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict. Ignoring CMI by monitoring only the untreated patients--whose outcomes remain unaltered--can inflate false alarm rates, because… 

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