用乘法门控提升医疗编码可解释性，性能媲美最强模型。
ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding
- 用乘法门控替代传统概念瓶颈，保留丰富语义表征
- 在MIMIC-IV数据集上F1、AUC与排名指标均达领先水平
- 兼顾高精度与医生可读的解释，适合临床辅助决策场景
从临床出院记录自动编码ICD-10需要兼具长尾多标签分类准确性与临床可解释性的模型。概念瓶颈模型（CBMs）通过人类可理解的概念路径实现透明预测，但将丰富文本表征压缩到狭窄概念层会阻碍梯度流动并限制预测能力。本文提出ShifaMind，一种基于乘法概念瓶颈（MCB）的架构，不改变瓶颈宽度而改变其形式：在保持标量概念接口的同时，对概念基表示施加可学习的乘法门控。在MIMIC-IV top-50 ICD-10编码任务中，ShifaMind在F1、AUC及排名指标上表现媲美最强基线LAAT，优于五个其他编码模型，并提供概念中介解释。相较于容量匹配的普通CBM，其在预测性能与可解释性指标上均有显著提升，凸显瓶颈设计的关键作用。
原文摘要 · Abstract (English)
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. We present ShifaMind, a concept-grounded architecture built around a Multiplicative Concept Bottleneck (MCB), which changes the form, rather than the width, of the bottleneck. Instead of projecting through a narrow concept layer, ShifaMind uses a learned multiplicative gate over a concept-grounded representation while retaining a scalar concept interface for inspection. On MIMIC-IV top-50 ICD-10 coding, ShifaMind achieves performance competitive with LAAT, the strongest baseline, across F1, AUC, and ranking metrics, while outperforming five additional ICD-coding baselines and providing concept-mediated explanations. Its substantial gains over a capacity-matched Vanilla CBM in both predictive performance and interpretability-oriented metrics highlight the importance of the bottleneck design.
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