Börner, N.; Schoenberg, M.B.; Pöschke, P.; Heiliger, C.; Jacob, S.; Koch, D.; Pöllmann, B.; Drefs, M.; Koliogiannis, D.; Böhm, C.;
et al. A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation. J. Clin. Med. 2022, 11, 6422.
https://doi.org/10.3390/jcm11216422
AMA Style
Börner N, Schoenberg MB, Pöschke P, Heiliger C, Jacob S, Koch D, Pöllmann B, Drefs M, Koliogiannis D, Böhm C,
et al. A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation. Journal of Clinical Medicine. 2022; 11(21):6422.
https://doi.org/10.3390/jcm11216422
Chicago/Turabian Style
Börner, Nikolaus, Markus B. Schoenberg, Philipp Pöschke, Christian Heiliger, Sven Jacob, Dominik Koch, Benedikt Pöllmann, Moritz Drefs, Dionysios Koliogiannis, Christian Böhm,
and et al. 2022. "A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation" Journal of Clinical Medicine 11, no. 21: 6422.
https://doi.org/10.3390/jcm11216422
APA Style
Börner, N., Schoenberg, M. B., Pöschke, P., Heiliger, C., Jacob, S., Koch, D., Pöllmann, B., Drefs, M., Koliogiannis, D., Böhm, C., Karcz, K. W., Werner, J., & Guba, M.
(2022). A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation. Journal of Clinical Medicine, 11(21), 6422.
https://doi.org/10.3390/jcm11216422