OS-024 The gender-equity model for liver allocation built on artificial intelligence (GEMA-AI) improves outcome predictions among liver transplant candidates

Áreas de investigación:

• Sin categoría

Año:

2024

Tipo de publicación:

Artículo en conferencia

Autores:

• Rodríguez-Perálvez, Manuel
• Gómez-Orellana, Antonio Manuel
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Majumdar, Avik
• McCaughan, Geoff
• Taylor, Rhiannon
• Hervás-Martínez, César
• Tsochatzis, Emmanuel

Volumen:

80

Título del libro:

Journal of Hepatology

Mes:

Junio

ISSN:

1600-0641

BibTex:

@conference{OS-024_Gender_equity_2024, author = "Manuel Rodr{\'i}guez-Per{\'a}lvez and Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and Avik Majumdar and Geoff McCaughan and Rhiannon Taylor and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Emmanuel Tsochatzis", abstract = "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. We aimed to develop and externally validate the Gender- Equity Model for Liver Allocation built on Artificial Intelligence (GEMA-AI) to predict waiting list outcomes in candidates for LT.", booktitle = "Journal of Hepatology", doi = "https://doi.org/10.1016/S0168-8278(24)00465-3", issn = "1600-0641", month = "Junio", title = "{OS}-024 {T}he gender-equity model for liver allocation built on artificial intelligence ({GEMA}-{AI}) improves outcome predictions among liver transplant candidates", url = "doi.org/10.1016/S0168-8278(24)00465-3", volume = "80", year = "2024", }

Abstract:

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. We aimed to develop and externally validate the Gender- Equity Model for Liver Allocation built on Artificial Intelligence (GEMA-AI) to predict waiting list outcomes in candidates for LT.