Predicting blood transfusions for coronary artery bypass graft patients using deep neural networks and synthetic data
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00521-024-10309-9.pdf
Reference32 articles.
1. Horvath K, Acker M, Chang H, Bagiella E et al (2013) Blood transfusion and infection after cardiac surgery. Ann Thorac Surg 95(6):2194–2201. https://doi.org/10.1016/j.athoracsur.2012.11.078
2. Kaur D, Sobiesk M, Patil S, Liu J, Bhagat P, Gupta A, Markuzon N (2021) Application of Bayesian networks to generate synthetic health data. J Am Med Inform Assoc 28(4):801–811. https://doi.org/10.1093/jamia/ocaa303
3. Li Q, Lv H, Chen Y, Shen J, Shi J, Zhou C (2024) Development and validation of a machine learning prediction model for perioperative red blood cell transfusions in cardiac surgery. Int J Med Inform 184:105343. https://doi.org/10.1016/j.ijmedinf.2024.105343
4. Mufarrih S, Mahmood F, Qureshi N, Yunus R, Matyal R et al (2023) Timing of blood transfusions and 30-days patient outcomes after coronary artery bypass graft surgery. J Cardiothorac Vasc Anesth 37(3):382–391. https://doi.org/10.1053/j.jvca.2022.11.029
5. Gao Y, Liu X, Wang L, Wang S, Yu Y, Ding Y, Wang J, Ao H (2022) Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting. Front Cardiovasc Med 9:881881. https://doi.org/10.3389/fcvm.2022.881881
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