Towards Automated ICD Coding Using Deep Learning
Table 2: Performance on different models
['Model Architecture', 'F1', 'AUC_ROC']
We can observe that with the Soft-attention mechanism, the F1 and AUC_ROC increase 5.2 and 2.3 percent, respectively, compared to the Hard-selection model. To further explore the efficacy of different modules in our model, we perform an ablation study on our intact Soft-attention model, which leverages character-level and word-level LSTM encoder and Soft-attention mechanism. Performance decreasing on several ablation experiments indicates that all the designed modules in our model are necessary and play a crucial role in the coding process.