Improving hospital readmission prediction using individualized utility analysis.

Journal: Journal of biomedical informatics
Published Date: Jun 1, 2021

Abstract

OBJECTIVE: Machine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources. Our objective was to determine whether ML models for allocating readmission-mitigating interventions have different usefulness based on their overall utility and discriminative ability.

Authors

  • Michael Ko
    Department of Statistics, Stanford University, Stanford, USA.
  • Emma Chen
    Department of Computer Science, Stanford University, CA, USA.
  • Ashwin Agrawal
    Department of Computer Science, Stanford University, CA, USA.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Anand Avati
    Department of Computer Science, Stanford University, Stanford, CA, USA. avati@cs.stanford.edu.
  • Andrew Ng
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Sanjay Basu
    Center for Primary Care and Outcomes Research, Center for Population Health Sciences, Departments of Medicine and Health Research and Policy, Stanford University, Palo Alto, CA basus@stanford.edu.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

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