Affiliation:
1. University of Tennessee: Bredesen Center for Interdisciplinary Research
2. University of Tennessee: Department of Industrial and Systems Engineering
3. Oak Ridge National Laboratory: Center for Nanophase Materials Sciences
Abstract
Reinforcement learning (RL) can assist in medical decision making using patient data collected in electronic health record (EHR) systems. RL, a type of machine learning, can use these data to develop treatment policies. However, RL models are typically trained using imperfect retrospective EHR data. Therefore, if care is not taken in training, RL policies can propagate existing bias in healthcare. Literature that considers and addresses the issues of bias and fairness in sequential decision making are reviewed. The major themes to mitigate bias that emerge relate to (1) data management; (2) algorithmic design; and (3) clinical understanding of the resulting policies.
Funder
Science Alliance, The University of Tennessee, and the Laboratory Directed Research
Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U.S. Department of Energy
Publisher
Association for Computing Machinery (ACM)
Subject
General Computer Science,Theoretical Computer Science
Cited by
8 articles.
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