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Diagnosing bias in data-driven algorithms for healthcare

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A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.

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References

  1. Nemati, S. et al. Crit. Care Med. 46, 547–553 (2018).

    Article  Google Scholar 

  2. Caruana, R. et al. in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1721–1730 (ACM, 2015).

  3. Bayati, M. et al. PLoS One 9, e109264 (2014).

    Article  Google Scholar 

  4. Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Science 366, 447–453 (2019).

    Article  CAS  Google Scholar 

  5. Schoenman, J. A. & Chockley, N. Understanding US health care spending. NICHM Foundation Data Brief (2011).

  6. National Academy of Medicine. Effective care for high-need patients. https://nam.edu/HighNeeds/highNeedPatients.html (2017).

  7. Benjamin, R. People’s Science: Bodies and Rights on the Stem Cell Frontier (Stanford University Press, 2013).

  8. Oh, J. et al. Infect. Control Hosp. Epidemiol. 39, 425–433 (2018).

    Article  Google Scholar 

  9. Liu, V. X. et al. Am. J. Respir. Crit. Care Med. 196, 856–863 (2017).

    Article  Google Scholar 

  10. Silver, D. et al. Nature 550, 354–359 (2017).

    Article  CAS  Google Scholar 

  11. Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C. & Faisal, A. A. Nat. Med. 24, 1716–1720 (2018).

    Article  CAS  Google Scholar 

  12. Schulam, P. & Saria, S. in Advances in Neural Information Processing Systems 30 (eds Guyon, I. et al.) 1697–1708 (Curran Associates, 2017).

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Author information

Authors and Affiliations

  1. Division of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    Jenna Wiens

  2. Law School, University of Michigan, Ann Arbor, MI, USA

    W. Nicholson Price II

  3. Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA

    Michael W. Sjoding

Authors
  1. Jenna Wiens
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  2. W. Nicholson Price II
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  3. Michael W. Sjoding
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Corresponding author

Correspondence to Jenna Wiens.

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The authors declare no competing interests.

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Wiens, J., Price, W.N. & Sjoding, M.W. Diagnosing bias in data-driven algorithms for healthcare. Nat Med 26, 25–26 (2020). https://doi.org/10.1038/s41591-019-0726-6

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  • DOI: https://doi.org/10.1038/s41591-019-0726-6

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