Fairness gaps in Machine learning models for hospitalization and emergency department visit risk prediction in home healthcare patients with heart failure.

Journal: International journal of medical informatics
PMID: 39106773

Abstract

OBJECTIVES: This study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess performance disparities, and propose solutions to improve model performance in diverse subpopulations.

Authors

  • Anahita Davoudi
    Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA.
  • Sena Chae
    College of Nursing, The University of Iowa, Iowa City, Iowa, USA.
  • Lauren Evans
    Center for Home Care Policy & Research, VNS Health, New York, NY, USA.
  • Sridevi Sridharan
    Center for Home Care Policy & Research, Visiting Nurse Service of New York, New York, NY.
  • Jiyoun Song
    School of Nursing, Columbia University, New York, New York, USA.
  • Kathryn H Bowles
    Visiting Nurse Service of New York, NY, USA; School of Nursing, University of Pennsylvania, PA, USA.
  • Margaret V McDonald
    The Visiting Nurse Service of New York, New York, NY, USA.
  • Maxim Topaz
    Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA.

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