Stan, A.; Călburean, P.-A.; Drinkal, R.-K.; Harpa, M.; Elkahlout, A.; Nicolae, V.C.; Tomșa, F.; Hadadi, L.; Brînzaniuc, K.; Suciu, H.;
et al. Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement. Diagnostics 2023, 13, 2907.
https://doi.org/10.3390/diagnostics13182907
AMA Style
Stan A, Călburean P-A, Drinkal R-K, Harpa M, Elkahlout A, Nicolae VC, Tomșa F, Hadadi L, Brînzaniuc K, Suciu H,
et al. Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement. Diagnostics. 2023; 13(18):2907.
https://doi.org/10.3390/diagnostics13182907
Chicago/Turabian Style
Stan, Alexandru, Paul-Adrian Călburean, Reka-Katalin Drinkal, Marius Harpa, Ayman Elkahlout, Viorel Constantin Nicolae, Flavius Tomșa, Laszlo Hadadi, Klara Brînzaniuc, Horațiu Suciu,
and et al. 2023. "Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement" Diagnostics 13, no. 18: 2907.
https://doi.org/10.3390/diagnostics13182907
APA Style
Stan, A., Călburean, P.-A., Drinkal, R.-K., Harpa, M., Elkahlout, A., Nicolae, V. C., Tomșa, F., Hadadi, L., Brînzaniuc, K., Suciu, H., & Mărușteri, M.
(2023). Inflammatory Status Assessment by Machine Learning Techniques to Predict Outcomes in Patients with Symptomatic Aortic Stenosis Treated by Transcatheter Aortic Valve Replacement. Diagnostics, 13(18), 2907.
https://doi.org/10.3390/diagnostics13182907