## 3 Experiment and Discussion
### Data Description  
We conducted experiments on the MIMIC-III dataset, which is a publicly available, multicenter, de-identified medical information database, which contains comprehensive clinical time series data and rich clinical notes for 46,520 patients, including important information such as vital sign monitoring signals, clinical notes, ICD-9 codes, disease severity, and clinical diagnosis. We refer to MIMIC-Extract [38] to screen the original data, using the patient's data from the previous 24 h and only considering clinical records with at least 30 h of current data. In addition, this paper preprocessed the samples; for missing values in the monitoring data, we used the mean interpolation method to fill in. In addition, we used the Z-score method to standardize all numerical features. Finally, categories that are relatively irrelevant to diagnosis, such as case management, rehabilitation services, and nutrition, were excluded, and patient samples without clinical records were removed. Finally, the total number of selected patient samples is 21,080, which are divided into training set (80%), validation set (10%), and test set (10%). The distribution information for the dataset is shown in Tables 1 and 2.  
We use a pre-trained clinical NER model med7 to extract medical-related keywords to improve the prediction performance of clinical tasks. The seven different medical entities extracted from clinical notes are shown in Table 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**DATA** & **\# Patient** & **\# Hospital** & **\# ICU** \\ \hline MIMIC-III (\textgreater{}15 years old) & 38,597 & 49,785 & 53,423 \\ MIMIC-Extract & 34,472 & 34,472 & 34,472 \\ MIMIC-Extract (at least & 23,937 & 23,937 & 23,937 \\
24 + 6 (gap) hours patient) & 21,080 & 21,080 & 21,080 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information of the MIMIC-III dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Mortality**} & \multicolumn{2}{c}{**Length of Stay (LOS)**} \\ \cline{2-5}  & **In-Hospital Mortality** & **In-ICU Mortality** & **LOS \textgreater{} 7** & **LOS \textgreater{} 3** \\ \hline ratio & 89.5\%:10.5\% & 93\%:7\% & 56.8\%:43.2\% & 92.1\%:7.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Class distribution of final cohort used in this paper.  
This work refers to MIMIC-Extract to define the target tasks as follows: (1) In-hospital mortality: patients who die during hospital stay after ICU admission. (2) In-ICU mortality: patients who die during ICU stay after ICU admission. (3) Length of stay > 3 (LOS > 3): patients who stay in the ICU longer than 3 days. (4) Length of stay > 7 (LOS > 7): patients who stay in the ICU longer than 7 days. Furthermore, since the dataset used in this paper suffers from class imbalance, we use AUROC and AUPRC to evaluate the performance of the model on imbalanced tasks. Here, AUROC is a commonly used robust metric for imbalanced datasets; AUPRC excludes true negatives from its calculation, making it particularly suitable for datasets with abundant negative instances. In order to evaluate the performance of the model more comprehensively, in addition to using the above metrics, we also compare the F1-score of different models in the same test set. We run all experiments five times with different initializations and report the mean and standard deviation values of the results. In this study, we use comparison models including MTL [39], MDCNN [40], CMN [41], BCB + LSTM [42], PM\({}^{2}\)F\({}^{2}\)N [43], and DESAM-cp [44]. The performance of different models is shown in Tables 4 and S1.