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

Artificial Intelligence in Medicine Breast Cancer Risk Conformal Prediction Uncertainty Quantification

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
25 Jan 2024
Historique:
medline: 25 1 2024
pubmed: 25 1 2024
entrez: 25 1 2024
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 38269963
pii: SHTI231113
doi: 10.3233/SHTI231113
doi:

Types de publication

Journal Article

Langues

eng

Pagination

991-995

Auteurs

Alexander S Millar (AS)

Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108, USA.

John Arnn (J)

Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108, USA.

Sam Himes (S)

Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108, USA.

Julio C Facelli (JC)

Department of Biomedical Informatics and Clinical and Translational Science Institute, The University of Utah, Salt Lake City, UT 84108, USA.

Classifications MeSH