## 3 Experiment and Discussion
### Results and Analysis  
The experimental results show that the proposed model significantly outperforms all baseline models across all prediction tasks. For In-hospital mortality prediction, our D4 Care model achieves improvements of 2.7-7.8% in AUROC and 3-15.8% in AUPRC compared to baseline models. Compared with the best comparative model DESAM-cp, AUROC and AUPRC increased by 2.7% and 3%, respectively, indicating that the proposed model can effectively extract multi-modal features for clinical outcome prediction. To \begin{table}
\begin{tabular}{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 > 7**} & \multicolumn{2}{c}{**LOS > 3**} \\ \cline{2-9}  & **AUROC** & **AUPRC** & **AUROC** & **AUROC** & **AUROC** & **AUROC** & **AUROC** & **AUPRC** \\ \hline \multirow{3}{*}{MTL} & 0.8623 & 0.5243 & 0.8611 & 0.4712 & 0.8211 & 0.6423 & 0.7605 & 0.8113 \\  & (\(\pm\)0.0143) & (\(\pm\)0.0140) & (\(\pm\)0.0123) & (\(\pm\)0.0088) & (\(\pm\)0.0012) & (\(\pm\)0.0123) & (\(\pm\)0.0043) & (\(\pm\)0.0034) \\ \cline{1-1}  & 0.8423 & 0.5067 & 0.8402 & 0.4548 & 0.8102 & 0.6236 & 0.7385 & 0.7925 \\ \cline{1-1}  & (\(\pm\)0.0042) & (\(\pm\)0.0051) & (\(\pm\)0.0044) & (\(\pm\)0.0062) & (\(\pm\)0.0012) & (\(\pm\)0.0052) & (\(\pm\)0.0095) & (\(\pm\)0.0026) \\ \cline{1-1}  & 0.8678 & 0.5403 & 0.8670 & 0.5032 & 0.8402 & 0.6332 & 0.7608 & 0.8308 \\ \cline{1-1}  & (\(\pm\)0.0092) & (\(\pm\)0.0012) & (\(\pm\)0.0012) & (\(\pm\)0.0014) & (\(\pm\)0.0012) & (\(\pm\)0.0016) & (\(\pm\)0.0048) & (\(\pm\)0.0053) \\ \cline{1-1}  & 0.8850 & 0.5860 & 0.8670 & 0.5322 & 0.8335 & 0.6520 & 0.7886 & 0.8210 \\ \cline{1-1}  & (\(\pm\)0.0021) & (\(\pm\)0.0048) & (\(\pm\)0.0012) & (\(\pm\)0.0036) & (\(\pm\)0.0019) & (\(\pm\)0.0085) & (\(\pm\)0.0023) & (\(\pm\)0.0019) \\ \cline{1-1}  & 0.8827 & 0.6178 & 0.8834 & 0.5750 & 0.8609 & 0.6974 & 0.8135 & 0.8492 \\ \cline{1-1}  & (\(\pm\)0.0034) & (\(\pm\)0.0034) & (\(\pm\)0.0023) & (\(\pm\)0.0028) & (\(\pm\)0.0008) & (\(\pm\)0.0011) & (\(\pm\)0.0010) & (\(\pm\)0.0036) \\ \cline{1-1}  & 0.8933 & 0.6344 & 0.8968 & 0.5822 & 0.8549 & 0.6820 & 0.8043 & 0.8345 \\ \cline{1-1}  & (\(\pm\)0.0016) & (\(\pm\)0.0023) & (\(\pm\)0.0035) & (\(\pm\)0.0021) & (\(\pm\)0.0094) & (\(\pm\)0.0028) & (\(\pm\)0.0028) & (\(\pm\)0.0032) \\ \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 4: Performances of different models on the same testing set. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entity Type** & **Total Entity** & **Unique Entity** & **Example** \\ \hline Drug & 742,231 & 18,204 & Magnesium \\ Strength & 152,234 & 10,680 & 400 mg/5 mL \\ Route & 207,876 & 1192 & PO \\ Dosage & 126,756 & 7230 & 30 mL \\ Form & 40,885 & 597 & suspension \\ Frequency & 71,285 & 3279 & bid \\ Duration & 5830 & 1185 & next 5 days \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The statistics of entities extracted from clinical notes. comprehensively evaluate model performance across multiple clinical prediction tasks, we use radar chart visualization, as shown in Figure 5. The results demonstrate that D4 Care exhibits comprehensive and consistent performance advantages across all prediction tasks, with its radial axes on the radar chart significantly outperforming other models, while maintaining balanced extension across all dimensions without any performance dips. Specifically, in the mortality prediction task, the model achieved AUROC scores of 0.9203 and 0.9189, with standard deviations below 0.005. This demonstrates exceptional sensitivity and reliability in critical care risk identification. In the task of predicting the length of stay, the prediction performance for short-term hospitalization (LOS > 3) was particularly outstanding (AUROC = 0.8484).