Interpretable machine learning-based predictive model for malnutrition in subacute post-stroke patients: an internal and external validation study

This study successfully developed and validated an ML model for efficiently screening malnutrition risk in patients with subacute stroke. The interpretable CAT-based model serves as a clinically actionable tool, enabling early stratification of malnutrition risk in subacute stroke patients. This facilitates the implementation of targeted nutritional interventions and personalized rehabilitation strategies, potentially improving outcomes in this vulnerable population.