Improving prediction of heart transplantation outcome using deep learning techniques
Author:
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-018-21417-7.pdf
Reference24 articles.
1. Klein, A. S. et al. Organ donation and utilization in the United States, 1999–2008. Am J Transplant 10, 973–986, https://doi.org/10.1111/j.1600-6143.2009.03008.x (2010).
2. Nilsson, J., Algotsson, L., Höglund, P., Lührs, C. & Brandt, J. Comparison of 19 pre-operative risk stratification models in open-heart surgery. Eur Heart J 27, 867–874, https://doi.org/10.1093/eurheartj/ehi720 (2006).
3. Weiss, E. S. et al. Development of a quantitative donor risk index to predict short-term mortality in orthotopic heart transplantation. The Journal of Heart and Lung Transplantation 31, 266–273 (2012).
4. Hong, K. N. et al. Who is the high-risk recipient? Predicting mortality after heart transplant using pretransplant donor and recipient risk factors. The Annals of thoracic surgery 92, 520–527 (2011).
5. Weiss, E. S. et al. Creation of a Quantitative Recipient Risk Index for Mortality Prediction After Cardiac Transplantation (IMPACT). The Annals of Thoracic Surgery 92, 914–922 (2011).
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