A Label Attention Model for ICD Coding from Clinical Text
Table 4: Ablation results on the MIMIC-III-full validation set. LAATCAML: A LAAT variant using the label attention mechanism proposed in CAML instead of our proposed label attention mechanism. CAMLLAAT: We modify CAML to use our label attention mechanism instead of the original one in CAML. LAATGRU: A LAAT variant using BiGRU instead of BiLSTM to learn latent feature vectors representing input words. All the score differences between LAAT and others are significant (p<0.01).
['Model', 'AUC Macro', 'AUC Micro', 'F1 Macro', 'F1 Micro', 'P@k P@5', 'P@k P@8', 'P@k P@15']
To better understand the model influences, we performed an ablation study on the validation set of the MIMIC-III-full dataset. In particular, for the first setting, namely LAATCAML, we couple the label attention mechanism proposed by \citeauthormullenbach2018 \shortcitemullenbach2018 with our BiLSTM encoder. We further investigate a variant of LAAT, namely LAATGRU, using a BiGRU encoder instead of a BiLSTM encoder. The reason might be that LSTM with the separate memory cells can theoretically remember longer-term dependencies than GRU, thus LSTM is more suitable for ICD coding from long clinical text, e.g. the discharge summaries which are typically long.