## 3 Results
### Prediction of the Mortality  
In our study, we employed a 10-fold cross-validation method to construct a predictive model for HF patients, forecasting outcomes at 3 days, 30 days, and 365 days. The model utilized data collected within the 24 h preceding admission to the ICU, in conjunction with physicians' treatment plans and test records, including the 365-day mortality rate. The dataset was split, with 80% used for model training and 20% for testing, followed by comprehensive statistical analyses to assess performance. The results of these analyses, detailing the AUROC scores for various classifiers with and without the inclusion of qualitative data, are presented in Table 2.  
Table 2 presents the AUROC values obtained from our study, showcasing the performance of six distinct machine-learning methods. To assess the impact of incorporating BERTopic topics on predicting HF-patient mortality in the ICU, we compared models using only quantitative data with those utilizing both quantitative and BERTopic data within the initial 24 h of ICU admission. Our findings reveal that integrating textual discharge summaries for new patients enhances the accuracy of mortality-rate predictions. Figure 3 displays the AUROC curves for various machine-learning methods, highlighting the effect of incorporating qualitative data on predictive performance. Figure 4 further illustrates the improvement in model applicability achieved through the integration of textual and quantitative data. The inclusion of qualitative data results in a substantial enhancement of AUROC scores, demonstrating that the models exhibit superior effectiveness in predicting mortality across different machine-learning methods and temporal intervals. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Terms**} & \multirow{2}{*}{**Method**} & **Without** & **With** \\  & & **Qualitative Data** & **Qualitative Data** \\ \hline \multirow{8}{*}{3 Days} & GaussianNB & 0.6349 \(\pm\) 0.0222 & 0.6691 \(\pm\) 0.0255 \\  & AdaBoost & 0.7666 \(\pm\) 0.0127 & 0.7708 \(\pm\) 0.0143 \\  & Bagging & 0.7425 \(\pm\) 0.0111 & 0.7522 \(\pm\) 0.0168 \\  & Gradient Boosting & 0.7725 \(\pm\) 0.0071 & 0.7816 \(\pm\) 0.0171 \\  & MLP Classifier & 0.7492 \(\pm\) 0.0047 & 0.7573 \(\pm\) 0.0073 \\  & LightGBM & 0.7935 \(\pm\) 0.0059 & 0.8218 \(\pm\) 0.0100 \\  & XGBoost & 0.7986 \(\pm\) 0.0063 & 0.8228 \(\pm\) 0.0117 \\ \hline \multirow{8}{*}{30 Days} & GaussianNB & 0.6391 \(\pm\) 0.0066 & 0.6616 \(\pm\) 0.0137 \\  & AdaBoost & 0.7335 \(\pm\) 0.0090 & 0.7469 \(\pm\) 0.0113 \\ \cline{1-1}  & Bagging & 0.7316 \(\pm\) 0.0054 & 0.7541 \(\pm\) 0.0031 \\ \cline{1-1}  & Gradient Boosting & 0.7385 \(\pm\) 0.0141 & 0.7580 \(\pm\) 0.0066 \\ \cline{1-1}  & MLP Classifier & 0.7237 \(\pm\) 0.0119 & 0.7390 \(\pm\) 0.0184 \\ \cline{1-1}  & LightGBM & 0.7436 \(\pm\) 0.0081 & 0.7599 \(\pm\) 0.0060 \\ \cline{1-1}  & XGBoost & 0.7389 \(\pm\) 0.0132 & 0.7553 \(\pm\) 0.0027 \\ \hline \multirow{8}{*}{365 Days} & GaussianNB & 0.6311 \(\pm\) 0.0049 & 0.6524 \(\pm\) 0.0112 \\ \cline{1-1}  & AdaBoost & 0.6872 \(\pm\) 0.0206 & 0.7047 \(\pm\) 0.0036 \\ \cline{1-1}  & Bagging & 0.7096 \(\pm\) 0.0178 & 0.7128 \(\pm\) 0.0033 \\ \cline{1-1}  & Gradient Boosting & 0.6905 \(\pm\) 0.0250 & 0.6919 \(\pm\) 0.0077 \\ \cline{1-1}  & MLP Classifier & 0.6721 \(\pm\) 0.0144 & 0.6793 \(\pm\) 0.0035 \\ \cline{1-1}  & LightGBM & 0.6310 \(\pm\) 0.0057 & 0.6508 \(\pm\) 0.0055 \\ \cline{1-1}  & XGBoost & 0.6003 \(\pm\) 0.0045 & 0.6125 \(\pm\) 0.0150 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: AUROC of different classifiers. Figure 4: AUROC of machine-learning methods with and without qualitative data. Figure 3: ROC curves for the different classifiers. This study shows that integrating quantitative and textual data significantly improves mortality predictions for HF patients in the ICU.