Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.
- DOI
- 10.1111/eci.14391
- Published
- 2025 Apr
- Container
- European journal of clinical investigation
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1111/eci.14391,
title = {Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.},
author = {Lehtonen E and Teuho J and Vatandoust M and Knuuti J and Knol RJJ and van der Zant FM and Juárez-Orozco LE and Klén R},
year = {2025},
journal = {European journal of clinical investigation},
doi = {10.1111/eci.14391},
url = {https://doi.org/10.1111/eci.14391}
}RIS
TY - JOUR TI - Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study. AU - Lehtonen E AU - Teuho J AU - Vatandoust M AU - Knuuti J AU - Knol RJJ AU - van der Zant FM AU - Juárez-Orozco LE AU - Klén R PY - 2025 JO - European journal of clinical investigation DO - 10.1111/eci.14391 UR - https://doi.org/10.1111/eci.14391 ER -
APA
E, L., J, T., M, V., J, K., RJJ, K., FM, V. D. Z., LE, J., & R, K. (2025). Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.. European journal of clinical investigation. https://doi.org/10.1111/eci.14391
Source records
- pubmed · retrieved 2026-09-26T01:11:35.131Z