Explainable artificial neural networks improve the performance of the Gender-Equity Model for liver Allocation (GEMA) to prioritize candidates for liver transplantation
Hits: 14
- Áreas de investigación:
- Sin categoría
- Año:
- 2024
- Tipo de publicación:
- Artículo en conferencia
- Autores:
-
- Rodríguez-Perálvarez, M.
- Gómez-Orellana, Antonio Manuel
- Guijo-Rubio, David
- Gutiérrez, Pedro Antonio
- Majumdar, A.
- McCaughan, G.
- Taylor, R.
- Tsochatzis, E. A.
- Hervás-Martínez, César
- Volumen:
- 30
- Título del libro:
- ILTS Annual Congress 2024
- Páginas:
- 1-309
- Mes:
- Septiembre
- Nota:
- (JCR: 5.1)
- Abstract:
- Background: Current prioritization models for liver transplantation (LT) are hampered by their linear nature, which does not fully capture the severity of patients with extreme analytical values. Methods: Cohort study including adult patients who qualified for elective LT in the United Kingdom (2010-2020, model training and internal validation) and in two Australian institutions (1998-2020, external validation). The Gender-Equity model for Liver Allocation corrected by serum sodium (GEMA-Na) was compared with a shallow artificial neural network optimized by neuroevolution and hybridization (GEMA-AI) using the same input variables. The primary outcome was mortality or delisting for sickness within the first 90 days. Discrimination was assessed by Harrell’s c-statistic (Hc).
- Comentarios:
- (JCR: 5.1)
