  Automatic International Classification of Diseases (ICD) coding is defined as
a kind of text multi-label classification problem, which is difficult because
the number of labels is very large and the distribution of labels is
unbalanced. The label-wise attention mechanism is widely used in automatic ICD
coding because it can assign weights to every word in full Electronic Medical
Records (EMR) for different ICD codes. However, the label-wise attention
mechanism is computational redundant and costly. In this paper, we propose a
pseudo label-wise attention mechanism to tackle the problem. Instead of
computing different attention modes for different ICD codes, the pseudo
label-wise attention mechanism automatically merges similar ICD codes and
computes only one attention mode for the similar ICD codes, which greatly
compresses the number of attention modes and improves the predicted accuracy.
In addition, we apply a more convenient and effective way to obtain the ICD
vectors, and thus our model can predict new ICD codes by calculating the
similarities between EMR vectors and ICD vectors. Extensive experiments show
the superior performance of our model. On the public MIMIC-III dataset and
private Xiangya dataset, our model achieves micro f1 of 0.583 and 0.806,
respectively, which outperforms other competing models. Furthermore, we verify
the ability of our model in predicting new ICD codes. The case study shows how
pseudo label-wise attention works, and demonstrates the effectiveness of pseudo
label-wise attention mechanism.
