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Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification.

Publication ,  Chapter
Millar, AS; Arnn, J; Himes, S; Facelli, JC
January 25, 2024

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds.

Duke Scholars

DOI

Publication Date

January 25, 2024

Volume

310

Start / End Page

991 / 995

Related Subject Headings

  • Uncertainty
  • Medicine
  • Medical Informatics
  • Humans
  • Female
  • Breast Neoplasms
  • Breast
  • Artificial Intelligence
  • 4601 Applied computing
  • 4203 Health services and systems
 

Citation

APA
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ICMJE
MLA
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Millar, A. S., Arnn, J., Himes, S., & Facelli, J. C. (2024). Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification. (Vol. 310, pp. 991–995). https://doi.org/10.3233/SHTI231113
Millar, Alexander S., John Arnn, Sam Himes, and Julio C. Facelli. “Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification.,” 310:991–95, 2024. https://doi.org/10.3233/SHTI231113.
Millar, Alexander S., et al. Uncertainty in Breast Cancer Risk Prediction: A Conformal Prediction Study of Race Stratification. Vol. 310, 2024, pp. 991–95. Pubmed, doi:10.3233/SHTI231113.

DOI

Publication Date

January 25, 2024

Volume

310

Start / End Page

991 / 995

Related Subject Headings

  • Uncertainty
  • Medicine
  • Medical Informatics
  • Humans
  • Female
  • Breast Neoplasms
  • Breast
  • Artificial Intelligence
  • 4601 Applied computing
  • 4203 Health services and systems