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Abstract Background and objectives: Predicting mortality in very low-birth-weight (VLBW) infants remains a critical challenge in neonatology. While traditional scoring systems and standard machine learning models are widely used, they often fail to capture the complex, non-linear interactions and time-dependent dynamics of neonatal clinical courses.
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