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- Title
An adversarial training framework for mitigating algorithmic biases in clinical machine learning.
- Authors
Yang, Jenny; Soltan, Andrew A. S.; Eyre, David W.; Yang, Yang; Clifton, David A.
- Abstract
Machine learning is becoming increasingly prominent in healthcare. Although its benefits are clear, growing attention is being given to how these tools may exacerbate existing biases and disparities. In this study, we introduce an adversarial training framework that is capable of mitigating biases that may have been acquired through data collection. We demonstrate this proposed framework on the real-world task of rapidly predicting COVID-19, and focus on mitigating site-specific (hospital) and demographic (ethnicity) biases. Using the statistical definition of equalized odds, we show that adversarial training improves outcome fairness, while still achieving clinically-effective screening performances (negative predictive values >0.98). We compare our method to previous benchmarks, and perform prospective and external validation across four independent hospital cohorts. Our method can be generalized to any outcomes, models, and definitions of fairness.
- Subjects
HOSPITALS; PREDICTIVE tests; COVID-19; MACHINE learning; ACQUISITION of data; CONCEPTUAL structures; BENCHMARKING (Management); DECISION making; CULTURAL prejudices; ARTIFICIAL neural networks; RESEARCH bias; PREDICTION models; ALGORITHMS
- Publication
NPJ Digital Medicine, 2023, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-023-00805-y