Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.

Bataille V, Panh L, Puymirat E, Cayla G, Motreff P, Lemesle G, Schiele F, Simon T, Danchin N, Ferrières J

DOI: 10.3389/frai.2026.1886402
Published: 2026
Container: Frontiers in artificial intelligence
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/frai.2026.1886402,
  title = {Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.},
  author = {Bataille V and Panh L and Puymirat E and Cayla G and Motreff P and Lemesle G and Schiele F and Simon T and Danchin N and Ferrières J},
  year = {2026},
  journal = {Frontiers in artificial intelligence},
  doi = {10.3389/frai.2026.1886402},
  url = {https://doi.org/10.3389/frai.2026.1886402}
}

RIS

TY  - JOUR
TI  - Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.
AU  - Bataille V
AU  - Panh L
AU  - Puymirat E
AU  - Cayla G
AU  - Motreff P
AU  - Lemesle G
AU  - Schiele F
AU  - Simon T
AU  - Danchin N
AU  - Ferrières J
PY  - 2026
JO  - Frontiers in artificial intelligence
DO  - 10.3389/frai.2026.1886402
UR  - https://doi.org/10.3389/frai.2026.1886402
ER  -

APA

V, B., L, P., E, P., G, C., P, M., G, L., F, S., T, S., N, D., & J, F. (2026). Unsupervised clustering identifies distinct phenotypes in acute myocardial infarction: insights from the FAST-MI 2015 registry.. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2026.1886402

Source records

pubmed · retrieved 2026-09-25T13:25:50.178Z