Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference

The paper introduces ExtraCare, a domain adaptation model designed for clinical event prediction from electronic health records (EHR), which addresses the limitations of traditional black-box models by providing transparent, interpretable outputs. ExtraCare decomposes patient representations into invariant and covariant components, enforcing orthogonality during training to enhance prediction accuracy while allowing for human-understandable explanations linked to medical concepts. Evaluated on two real-world EHR datasets, ExtraCare outperforms existing feature alignment methods, making it a significant advancement for practitioners seeking reliable and explainable AI solutions in healthcare.