Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.

Toprak B, Solleder H, Di Carluccio E, Greenslade JH, Parsonage WA, Schulz K, Cullen L, Apple FS, Ziegler A, Blankenberg S, Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) group

Open source

DOI
10.1016/s2589-7500(24)00191-2
Published
2024 Oct
Container
The Lancet. Digital health
Publisher
Not recorded
Open access
unknown

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BibTeX

@article{allodium:10.1016/s2589-7500-24-00191-2,
  title = {Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.},
  author = {Toprak B and Solleder H and Di Carluccio E and Greenslade JH and Parsonage WA and Schulz K and Cullen L and Apple FS and Ziegler A and Blankenberg S and Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) group},
  year = {2024},
  journal = {The Lancet. Digital health},
  doi = {10.1016/s2589-7500(24)00191-2},
  url = {https://doi.org/10.1016/s2589-7500(24)00191-2}
}

RIS

TY  - JOUR
TI  - Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.
AU  - Toprak B
AU  - Solleder H
AU  - Di Carluccio E
AU  - Greenslade JH
AU  - Parsonage WA
AU  - Schulz K
AU  - Cullen L
AU  - Apple FS
AU  - Ziegler A
AU  - Blankenberg S
AU  - Artificial Intelligence in Suspected Myocardial Infarction Study (ARTEMIS) group
PY  - 2024
JO  - The Lancet. Digital health
DO  - 10.1016/s2589-7500(24)00191-2
UR  - https://doi.org/10.1016/s2589-7500(24)00191-2
ER  - 

APA

B, T., H, S., E, D. C., JH, G., WA, P., K, S., L, C., FS, A., A, Z., S, B., & group, A. I. I. S. M. I. S. (. (2024). Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: a retrospective study.. The Lancet. Digital health. https://doi.org/10.1016/s2589-7500(24)00191-2

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