Journal article
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
- Abstract:
- As models based on machine learning continue to be developed for healthcare applications, greater effort is needed to ensure that these technologies do not reflect or exacerbate any unwanted or discriminatory biases that may be present in the data. Here we introduce a reinforcement learning framework capable of mitigating biases that may have been acquired during data collection. In particular, we evaluated our model for the task of rapidly predicting COVID-19 for patients presenting to hospital emergency departments and aimed to mitigate any site (hospital)-specific and ethnicity-based biases present in the data. Using a specialized reward function and training procedure, we show that our method achieves clinically effective screening performances, while significantly improving outcome fairness compared with current benchmarks and state-of-the-art machine learning methods. We performed external validation across three independent hospitals, and additionally tested our method on a patient intensive care unit discharge status task, demonstrating model generalizability.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.2MB, Terms of use)
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- Publisher copy:
- 10.1038/s42256-023-00697-3
Authors
- Publisher:
- Springer Nature
- Journal:
- Nature Machine Intelligence More from this journal
- Volume:
- 5
- Issue:
- 8
- Pages:
- 884–894
- Place of publication:
- England
- Publication date:
- 2023-07-31
- Acceptance date:
- 2023-06-27
- DOI:
- EISSN:
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2522-5839
- Pmid:
-
37615031
- Language:
-
English
- Keywords:
- Pubs id:
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1499411
- Local pid:
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pubs:1499411
- Deposit date:
-
2023-09-12
Terms of use
- Copyright holder:
- Yang et al.
- Copyright date:
- 2023
- Rights statement:
- © The Author(s) 2023. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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