Applications - Donor-recipient matching in liver transplants

Members

Description

• Collaborator Institution:  Spanish Inter-Ministerial Commision of Science and Technology, European Regional Development fund, “Junta de Andalucia” (Spain) and Spanish Ministry of Education and Science. Transplant Unit of the Reina Sofia Hospital, Córdoba (Spain) and Astellas Pharma Company.
• Main targeted goal: Computational tools for the decision-making in liver transplantation.
• Methodological subfield: Supervised Classification, Logistic Regression,    Artificial Neural Networks, Evolutionary Computation, Metaheuristics, Decision-making process, Rule-based Systems.
• Methodological contribution: New Artificial Neural Networks models with projection and kernel basis functions, Memetic Evolutionary Algorithms for unbalanced data, Hybrid models, Rule-based Systems and Oversampling techniques.
• Impact in domain field:  New decision support system which lead to making the correct decision about receptor choice based on efficient and impartial criteria (principles of justice, efficiency and equity). A Rule-based system that aids medical experts in making decisions about the allocation of liver transplants.

Publications

• Gender-Equity Model for Liver Allocation using Artificial Intelligence (GEMA-AI) for waiting list liver transplant prioritization
• Machine learning algorithms in controlled donation after circulatory death under normothermic regional perfusion: A graft survival prediction model
• Emparejamiento donante-receptor durante la donación en asistolia controlada con perfusión regional normotérmica: papel de los clasificadores de machine learning como modelos predictivos de la supervivencia del injerto
• Artificial intelligence and liver transplantation: Looking for the bestdonor-recipient pairing
• New models for donor-recipient matching in lung transplantations