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- Evgeni Mekov and Viktoria Ilieva.
- Department of Occupational Diseases, Faculty of Medicine, Medical University - Sofia, Sofia, Bulgaria.
- Presse Med. 2022 Dec 1; 51 (4): 104140104140.
AbstractLung transplantation has been accepted as a viable treatment for end-stage respiratory failure. While regression models continue to be a standard approach for attempting to predict patients' outcomes after lung transplantation, more sophisticated supervised machine learning (ML) techniques are being developed and show encouraging results. Transplant clinicians could utilize ML as a decision-support tool in a variety of situations (e.g. waiting list mortality, donor selection, immunosuppression, rejection prediction). Although for some topics ML is at an advanced stage of research (i.e. imaging and pathology) there are certain topics in lung transplantation that needs to be aware of the benefits it could provide.Copyright © 2022 Elsevier Masson SAS. All rights reserved.
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