AI RESEARCH
ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding
arXiv CS.LG
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ArXi:2605.08482v1 Announce Type: new Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and interpretable to clinicians. Concept Bottleneck Models (CBMs) offer a principled framework for interpretability by routing predictions through human-interpretable concepts, but this transparency often comes at a cost: compressing rich clinical text representations into a narrow concept layer can restrict gradient flow and limit predictive capacity.