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Recalibrating prognostic models to improve predictions of in-hospital child mortality in resource-limited settings.

Morris OgeroJohn NdirituRachel SargutaTimothy TutiJalemba AluvaalaSamuel Akech
Published in: Paediatric and perinatal epidemiology (2023)
Even after model adjustment, the calibration performances of the 4 models did not meet the recommended threshold for perfect calibration. This finding is suggestive of models over/underestimating the predicted risk of in-hospital mortality, potentially harmful clinically. Therefore, researchers may consider other alternatives, such as ensemble techniques to combine these models into a meta-model to improve out-of-sample predictive performance.
Keyphrases
  • healthcare
  • type diabetes
  • cardiovascular disease
  • cardiovascular events
  • risk factors
  • machine learning
  • deep learning
  • drug induced