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Cited by 55Breast Cancer Treatment StudiesAI in cancer detectionMachine Learning in Healthcare

Machine learning to guide the use of adjuvant therapies for breast cancer

Open access
Alaa, Ahmed M., Gurdasani, Deepti, Harris, Adrian L., Rashbass, Jem, van der Schaar, Mihaela·2021·Nature Machine Intelligence
DOI 10.1038/s42256-021-00353-8
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Crossref · 10.1038/s42256-021-00353-8

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  • A study investigating survival benefits of adjuvant therapy in breast cancer evaluated ML models originally developed using UK populations in US clinical settings.

    “For external validation of therapeutic ML models, a pertinent reference is a study investigating the survival benefits of adjuvant therapy in breast cancer where researchers evaluated ML models, which were originally developed using populations from the United Kingdom, in clinical settings in the United States [11].”

    From Toward real‐world deployment of machine learning for health care: External validation, continual monitoring, and randomized clinical trials (2024) · cites this paper as [11]

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References 53 · Cited by 55

Authors
Alaa, A. M., Gurdasani, D., Harris, A. L., Rashbass, J., & van der Schaar, M.
Affiliations
  • University of California, Los Angeles (US)
  • Queen Mary University of London (GB)
  • University of Oxford (GB)
  • Public Health England (GB)
ORCID
Venue
Nature Machine Intelligence
Publisher
Springer Science and Business Media LLC
Published
2021-06-24
Volume
3
Issue
8
Pages
716-726
ISSN
2522-5839 (ISSN-L)
DOI
10.1038/s42256-021-00353-8
OpenAlex
W3114973470
Cited by
55
References
53
Citations by year
2026: 1 · 2025: 13 · 2024: 15 · 2023: 17 · 2022: 6 · 2021: 3
FWCI
4.10
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Machine Intelligence (publishedVersion)
  • Research Square (acceptedVersion, cc-by)
Funders
  • University of Cambridge
  • University of Oxford
  • Medical Research Council (MR/S003711/1, MR/S003711/2)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
breast cancer, adjuvant therapy, treatment response prediction, outcome prediction, early invasive breast cancer, model interpretability, clinical decision support, survival prediction, risk factors, external validation, cancer registry

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