## 2 Background Studies
### Deep-Learning-Based Mortality Prediction  
Deep-learning-based studies [45; 46; 47] in different areas becoming increasingly popular. [PERSON] et al. [46] created an explainable long short-term memory (LSTM) model to predict ICU mortality after 90 days. According to model interpretation, they discovered that input qualities might interact and compensate for one another, pulling towards survival in one direction and towards non-survival in the other direction. Using bidirectional LSTM, [PERSON] et al. [47] developed a new paradigm for mortality prediction. They discovered that the deep-learning-based approach outperforms the existing SAPS-II severity assessment system. Bidirectional extended short-term memory outperformed the other methods, owing to its ability to capture both forward and backward temporal dependencies. [PERSON] et al. [48] used deep learning to create a pediatric risk of mortality prediction tool. This study obtained an AUROC in the range of 0.89 to 0.97 for mortality prediction 6 to 60 h before death. By using LR and the extreme GB algorithm, the first in-hospital mortality prediction nomogram was developed. A deep multiscale convolutional neural network (CNN) architecture trained on the MIMIC-III dataset was proposed in [49] for mortality prediction. According to this study, the model has a AUROC of 0.8735 (0.0025), which is similar to the state of the art of deep learning mortality models trained on MIMIC-III data while still being interpretable. In the [5], in order to identify the most optimal lab events that contribute the most to mortality, an algorithm based on genetics and wrapper feature selection method was demonstrated. For experimental validation, the proposed model was compared to four popular traditional mortality assessments, as well as state-of-the-art ML models. In terms of the AUROC, the proposed model outperformed traditional scoring systems by 11-29% and state-of-the-art models by up to 14%. The authors of [50] used clinical data to construct a deep learning algorithm and a risk-score system for predicting ICU admission and in-hospital death in COVID-19 patients. The deep learning model predicted ICU admissions and mortality with AUROC values of 0.780 and 0.844, respectively. AUROCs of 0.728 and 0.848 were found for the associated risk ratings.  
Research works that have utilized different ML algorithms to predict the mortality rate in the ICU are summarized below in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Algorithm** & **Feature Selection** & **Interpretation** & **Dataset** & **Outcome** \\ \hline
[34] & RF & No & No & MIMIC-III & Accuracy: 0.73 \\
[35] & LR & XGBostig & No & MIMIC-III & AUROC: 0.8416 \\
[36] & Ensemble Method & No & No & MIMIC-III & AUROC: 83.91 \\
[38] & Ensemble Method & GA & No & MIMIC-III & Accuracy: 82.5\% \\
[41] & GBM & No & No & MIMIC-III & AUROC: 0.845 \\ \hline \end{tabular}
\end{table}
Table 1: Summary of ML-based research works.  
The analysis presented in this section shows that deep learning with interpretation has been utilized in related studies. Feature selection was proposed as a potential improvement in [49]. Therefore, we believe that this area of ICU mortality prediction can be expanded and enhanced.