# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 5 Results  
### Mimic-Iii  
Results are displayed for five different models. First, results for the two baseline models, **CNN** and **BiGRU**, are shown. Because in most of the experiments, the BiGRU models performed at least on par with their BiLSTM variants, we only report the results of **BiGRU** as a recurrent neural network baseline. The reason for this good performance of GRU models compared to LSTM models most likely resides in the amount of available training data for various target categories. Then, we report on three more advanced models as discussed in the Method section: **DR-CAML**, **MVC-LDA**, and **MVD-RLDA**. Different hyperparameter values are considered and tested on the development set of MIMIC-III the setting giving the highest average performance on the development set is reported in Table 2.  
Figure 7: Overview of hierarchical variant of a model, inspired by [34]. For all these models using their optimal hyperparameter settings, the average performance is reported in terms of Micro F1, Macro F1, Micro AUC (ROC), and Precision@X. For models that are only evaluated on the top 50 most frequent categories in the training data, results are displayed in Table 3. This experiment is then repeated over all categories, which leads to the results in Table 4. Last, Table 5 gives the results of training the models on all labels for the Full dataset. This experiment is repeated for the hierarchical variants of all described models. This time, only results on the top 50 most frequent target categories are reported in Table 6. As hierarchical models introduce a large number of additional intermediate categories, the target space is too large to train these hierarchical variants in a full category setting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Micro F1** & **Micro F1** & **Micro F1** & **Micro AUC** & **P@5** \\ \hline \multirow{4}{*}{**Dis**} & **CNN** & 51.01 & 40.80 & 42.58 & 84.38 & 59.48 \\ \cline{2-6}  & **GRU** & 53.88 & 40.86 & 43.40 & 85.36 & 61.59 \\ \cline{2-6}  & **DR-CAML** & 48.99 & 59.03 & 50.47 & 89.45 & 68.28 \\ \cline{2-6}  & **MVC-LDA** & 59.75 & 51.60 & 53.03 & 90.02 & 69.77 \\ \cline{2-6}  & **MVC-RLDA** & 58.84 & 50.74 & 52.10 & 89.77 & 68.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of flat models on all labels of the MIMIC-III Dis dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CNN** & **BiGRU** & **DRCAML** & **MVC-LDA** & **MVC-RLDA** \\ \hline
**\# of filters** & 500 & - & 50 & 6,8,10,12 & 6,8,10,12 \\ \hline
**Filter Sizes** & 4 & - & 10 & 70 & 90 \\ \hline \(\lambda\) & - & 0.0005 & - & - & 0.0005 \\ \hline
**Lr** & 0.003 & 0.003 & 0.0001 & 0.001 & 0.001 \\ \hline
**Batch size** & 16 & 16 & 16 & 4 & 4 \\ \hline
**Seq. length-** & 2500 & 2500 & 2500 & 10000 & 10000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Optimal hyperparameter values obtained on the development set of the MIMIC-III dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Micro F1** & **Micro F1** & **Micro F1** & **Micro AUC** & **P@5** \\ \hline \multirow{4}{*}{**Dis**} & **CNN** & 63.42 & 59.74 & 91.57 & 62.33 \\ \cline{2-6}  & **GRU** & 63.49 & 55.72 & 91.79 & 61.72 \\ \cline{2-6}  & **DR-CAML** & 69.64 & 64.56 & 93.90 & 65.39 \\ \cline{2-6}  & **MVC-LDA** & 69.07 & 64.17 & 93.69 & 65.15 \\ \cline{2-6}  & **MVC-RLDA** & 69.53 & 64.85 & 93.77 & 64.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of flat models on top 50 most frequent categories of the MIMIC-III Dis-50 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Micro F1** & **Micro F1** & **Micro F1** & **Micro AUC** & **P@5** \\ \cline{2-6}  & **CNN** & 53.88 & 40.86 & 43.40 & 85.36 & 61.59 \\ \cline{2-6}  & **DR-CAML** & 48.99 & 59.03 & 50.47 & 89.45 & 68.28 \\ \cline{2-6}  & **MVC-LDA** & 59.75 & 51.60 & 53.03 & 90.02 & 69.77 \\ \cline{2-6}  & **MVC-RLDA** & 58.84 & 50.74 & 52.10 & 89.77 & 68.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of flat models on all labels of the MIMIC-III Dis dataset.