Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases.

Journal: Journal of biomedical informatics
PMID: 38876453

Abstract

OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory conclusions. This study aims to evaluate racial disparities in predicting mortality among patients with chronic diseases using a fairness detection method that considers systematic differences.

Authors

  • Yifei Wang
    Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Liqin Wang
    Brigham and Women's Hospital, Boston, MA, USA.
  • Zhengyang Zhou
    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
  • John Laurentiev
    Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
  • Joshua R Lakin
    Harvard Medical School, Boston, Massachusetts.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.
  • Pengyu Hong
    Computer Science Department, Brandeis University, Waltham, MA, 02453, USA. hongpeng@brandeis.edu.

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