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Figure 7. Overview of machine learning applications in overall survival prediction (2021-2024). This figure illustrates the diverse applications of machine learning in predicting mortality and survival outcomes related to LT and associated conditions. (A) Distribution of studies across prediction categories, where each category represents specific survival problems such as mortality prediction in diabetic patients, survival in ACLF patients, cause of death analysis, overall survival at various time points, and post-transplant outcomes in patients with complications like malnutrition, infection, and sarcopenia; (B) Utilization of different machine learning algorithms (linear models, neural networks, and tree-based methods) across prediction tasks; (C) Model performance comparison using AUC and C-index metrics; (D) Data types employed across studies, showing the predominance of tabular data and specialized use of radiological imaging for specific conditions, such as in sarcopenia assessment. LT: Liver transplantation; ACLF: acute-on-chronic liver failure; AUC: area under the curve.