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Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.

Richard John WoodmanKimberley BryantMichael J SorichAlberto PilottoArduino Aleksander Mangoni
Published in: Journal of medical Internet research (2021)
The use of MPI domains with LR-MLE considerably improved the prediction accuracy compared with that obtained using the traditional 3-category MPI. The XGBoost ML algorithm slightly improved accuracy compared with LR-MLE, and adding clinical data improved accuracy. These results build on previous work on the MPI and suggest that implementing risk scores based on MPI domains and clinical data by using ML prediction models can support clinical decision-making with respect to risk stratification for the follow-up care of older hospitalized patients.
Keyphrases
  • machine learning
  • big data
  • decision making
  • physical activity
  • palliative care
  • quality improvement
  • deep learning
  • coronary artery disease