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Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation.

Khalid AlghataniNariman AmmarAbdelmounaam RezguiArash Shaban-Nejad
Published in: JMIR medical informatics (2021)
The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient's profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.
Keyphrases
  • intensive care unit
  • machine learning
  • case report
  • cardiovascular events
  • mechanical ventilation
  • deep learning
  • big data
  • coronary artery disease