Güldener, U.; Kessler, T.; von Scheidt, M.; Hawe, J.S.; Gerhard, B.; Maier, D.; Lachmann, M.; Laugwitz, K.-L.; Cassese, S.; Schömig, A.W.;
et al. Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography. J. Clin. Med. 2023, 12, 2941.
https://doi.org/10.3390/jcm12082941
AMA Style
Güldener U, Kessler T, von Scheidt M, Hawe JS, Gerhard B, Maier D, Lachmann M, Laugwitz K-L, Cassese S, Schömig AW,
et al. Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography. Journal of Clinical Medicine. 2023; 12(8):2941.
https://doi.org/10.3390/jcm12082941
Chicago/Turabian Style
Güldener, Ulrich, Thorsten Kessler, Moritz von Scheidt, Johann S. Hawe, Beatrix Gerhard, Dieter Maier, Mark Lachmann, Karl-Ludwig Laugwitz, Salvatore Cassese, Albert W. Schömig,
and et al. 2023. "Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography" Journal of Clinical Medicine 12, no. 8: 2941.
https://doi.org/10.3390/jcm12082941
APA Style
Güldener, U., Kessler, T., von Scheidt, M., Hawe, J. S., Gerhard, B., Maier, D., Lachmann, M., Laugwitz, K. -L., Cassese, S., Schömig, A. W., Kastrati, A., & Schunkert, H.
(2023). Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography. Journal of Clinical Medicine, 12(8), 2941.
https://doi.org/10.3390/jcm12082941
