Abstract
Hospital readmissions are a major burden on healthcare systems and result from complex interactions among heterogeneous clinical factors. While machine learning can model such interactions, interpretability is essential for clinical use. We propose a logic-guided learning framework for readmission prediction based on Logic Tensor Networks (LTN), integrating first-order logical rules into supervised learning using Italian administrative healthcare data. The rules encode clinically motivated relationships among chronic conditions, prior utilization, and patient stability. Training combines classification loss with rule-satisfaction constraints, enabling interpretable predicate representations. Results across cross-validation runs show competitive performance compared to baseline models, while the learned predicates provide clinically meaningful and interpretable structure for readmission risk.