Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models

BACKGROUND: Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53–0.64). Machine learning (ML) of...

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Detalles Bibliográficos
Autores principales: Ming, Chang, Viassolo, Valeria, Probst-Hensch, Nicole, Chappuis, Pierre O., Dinov, Ivo D., Katapodi, Maria C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585114/
https://www.ncbi.nlm.nih.gov/pubmed/31221197
http://dx.doi.org/10.1186/s13058-019-1158-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6585114/
https://www.ncbi.nlm.nih.gov/pubmed/31221197
http://dx.doi.org/10.1186/s13058-019-1158-4

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