## 3 Experiment and Discussion
### Ablation Study  
To evaluate the effectiveness of the module designed in this paper on the proposed D4 Care, we conducted ablation experiments on the test set. The experimental results are shown in Tables 5 and S2, and Figure 7.  
We evaluate the individual impact and joint contribution of models such as SAA, MDCA, DM, and MacLN on the model performance. In addition, we compared the performance of the model using single-modal data and multi-modal data; Only-TS means that only time series data is used. From the ablation experiment results, we can see that D4 Care outperforms other comparison models in all tasks, which proves the effectiveness of proposed models in this paper. Compared with Only-TS, which only uses time series data for modeling, the cross-modal fusion model demonstrates superior performance. In addition, when the model does not use the MDCA to learn the joint expression of cross-modal features, but simply fuses the temporal features and medical entities, the performance of the model decreases by 1%-4%, which proves that the MDCA designed \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**In-Hospital Mortality**} & \multicolumn{2}{c}{**In-ICU Mortality**} & \multicolumn{2}{c}{**LOS \textgreater{}7**} & \multicolumn{2}{c}{**LOS \textgreater{}3**} \\ \cline{2-10}  & **AUROC** & **AUPRC** & **AUROC** & **AUROC** & **AUROC** & **AUROC** & **AUROC** & **AUROC** \\ \hline \multirow{3}{*}{Only-TS} & 0.8420 & 0.5002 & 0.8436 & 0.4587 & 0.8010 & 0.6189 & 0.7432 & 0.7824 \\  & (\(\pm\)0.0065) & (\(\pm\)0.0082) & (\(\pm\)0.0032) & (\(\pm\)0.0010) & (\(\pm\)0.0013) & (\(\pm\)0.0034) & (\(\pm\)0.0027) & (\(\pm\)0.0031) \\ \cline{1-1}  & 0.8940 & 0.6358 & 0.8964 & 0.5702 & 0.8501 & 0.6822 & 0.8110 & 0.8322 \\ \cline{1-1}  & (\(\pm\)0.0008) & (\(\pm\)0.0014) & (\(\pm\)0.0024) & (\(\pm\)0.0027) & (\(\pm\)0.0092) & (\(\pm\)0.0023) & (\(\pm\)0.0024) & (\(\pm\)0.0019) \\ \cline{1-1}  & 0.9147 & 0.6448 & 0.9045 & 0.5842 & 0.8719 & 0.6914 & 0.8303 & 0.8446 \\ \cline{1-1}  & (\(\pm\)0.0011) & (\(\pm\)0.0027) & (\(\pm\)0.0031) & (\(\pm\)0.0018) & (\(\pm\)0.0010) & (\(\pm\)0.0011) & (\(\pm\)0.0015) & (\(\pm\)0.0011) \\ \cline{1-1}  & 0.9138 & 0.6460 & 0.9092 & 0.5851 & 0.8752 & 0.6920 & 0.8326 & 0.8490 \\ \cline{1-1}  & (\(\pm\)0.0017) & (\(\pm\)0.0028) & (\(\pm\)0.0012) & (\(\pm\)0.0008) & (\(\pm\)0.0016) & (\(\pm\)0.0044) & (\(\pm\)0.0023) & (\(\pm\)0.0017) \\ \cline{1-1} w/o MacLN & 0.9132 & 0.6401 & 0.9032 & 0.5788 & 0.8718 & 0.6811 & 0.8267 & 0.8434 \\ \cline{1-1}  & (\(\pm\)0.0082) & (\(\pm\)0.0056) & (\(\pm\)0.0019) & (\(\pm\)0.0021) & (\(\pm\)0.0032) & (\(\pm\)0.0024) & (\(\pm\)0.0020) & (\(\pm\)0.0024) \\ \cline{1-1}  & 0.9203 & 0.6645 & 0.9189 & 0.6038 & 0.8820 & 0.7048 & 0.8484 & 0.8557 \\ \cline{1-1}  & (\(\pm\)0.0028) & (\(\pm\)0.0014) & (\(\pm\)0.0045) & (\(\pm\)0.0033) & (\(\pm\)0.0056) & (\(\pm\)0.0012) & (\(\pm\)0.0011) & (\(\pm\)0.0022) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The ablation experiment results of D4 Care (“w/o” means not included, “Only-TS” means only using time series data, while other models use multi-modal data). Figure 7: Heatmap of ablation experiment results. in this chapter helps the model learn the potential correlation between cross-features and enhances the joint expression of multi-scale features. When the model does not consider the dependencies between sentences, that is, lacks the SAA, the performance of the model decreases. We speculate that if simple average pooling is performed on sentences, the model will tend to learn general features, while ignoring potential key information and important relationships in the text. The SAA allows the model to learn feature mappings of different subspaces, which helps capture the correlation between different sentences and enables the model to pay more attention to the fine-grained key features in clinical notes.