Causality-Informed Machine Learning for ICU Length of Stay Prediction: A Healthcare Case Study

Machine learning has shown great promise in advancing healthcare applications, particularly in predictive modeling for clinical outcomes. However, widespread adoption in medical practice remains limited due to critical concerns around explainability, reliability, and variability across institutions. To address these challenges, we apply a Causality-Informed Neural Network (CINN) framework and integrate expert-derived causal knowledge into the model architecture. By embedding causal relationships identified by physicians, CINN aims to enhance model transparency and generalizability across different clinical settings. In a case study focused on predicting the length of stay (LOS) in the Intensive Care Unit (ICU) for sepsis patients, we collaborate with medical experts to incorporate domain-specific causal structures. Experimental results highlight its superior explainability, robustness under distributional shifts, and improved transferability across cohorts. These findings suggest that CINN offers a promising pathway for building clinically aligned, actionable AI tools in healthcare.