## 4 Discussion  
This study presents a sepsis treatment recommendation system integrated with a next SOFA score prediction model. The paramount aspect of our research is the meticulous pursuit of the most precise next SOFA score prediction model, recognizing its pivotal role in the overall performance of the treatment system. The ensuing table provides a comprehensive overview of the RMSE results, shedding light on the predictive accuracy of the model (Table 1). These results show the predictive accuracy of each method in estimating the SOFA score, with the proposed approach demonstrating a superior performance. The evaluation employed standard assessment metrics, including the Concordance Correlation Coefficient (CCC) and R-square (Tables 2 and 3). The table clearly illustrates that the proposed method consistently outperforms the conventional approaches, yielding notably more accurate results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **RMSE** \\ \hline LogisticRegression & 2.572 \\ \hline LinearRegression & 2.079 \\ \hline DecisionTreeRegressor & 2.751 \\ \hline MLP & 2.0004 \\ \hline RandomForestRegressor & 1.988 \\ \hline Soft\_voting (logistic + decisiontree + LF) & 2.102 \\ \hline AdaBoost (5 LinearRegressions) & 2.182 \\ \hline GradientBoostingRegressor & 2.74 \\ \hline AdaBoost (3 MLPs) & 2.050 \\ \hline
**Proposed** & **1.946** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of RMSE scores for different methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **CCC** \\ \hline LogisticRegression & 0.512 \\ \hline LinearRegression & 0.606 \\ \hline DecisionTreeRegressor & 0.491 \\ \hline MLP & 0.653 \\ \hline RandomForestRegressor & 0.651 \\ \hline Soft\_voting (logistic+decisiontree+LF) & 0.621 \\ \hline AdaBoost (5 LinearRegressions) & 0.550 \\ \hline GradientBoostingRegressor & 0.179 \\ \hline AdaBoost (3 MLPs) & 0.638 \\ \hline
**Proposed** & **0.668** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of CCC scores for different methods. In the comparative analysis with physician-prescribed treatments, the proposed sepsis treatment recommendation model exhibited a similarity rate of 74.58182%. This indicates a significant alignment between the model's recommended treatments and those prescribed by the medical professionals. Such a high degree of similarity underscores the potential utility and effectiveness of the model in aiding clinical decision making for sepsis treatment. The following key aspects explain why our study claims superiority over the traditional reinforcement learning (RL) methods:  
**Sensitivity Analysis**: The proposed approach leverages sensitivity analysis to optimize the sepsis treatment dosages. This involves making minor adjustments to the dosage levels based on predictions from our deep learning model. The objective is to fine-tune the treatment strategies, rendering them more patient-specific and adaptive to individual patients' needs. **Data-Driven Approach**: The proposed method focuses on providing data-driven treatment recommendations based on the predicted Sequential Organ Failure Assessment (SOFA) scores. This approach acknowledges the varying responses of individual patients to the treatment. These variations can be significant and require personalized consideration, which traditional RL methods may not readily provide. **Overcoming RL Limitations**: In conventional RL, the creation of a virtual environment and agent to compute rewards for actions can be challenging in clinical settings. Our model overcomes this limitation by not relying on RL, making it more practical and applicable in real-world clinical scenarios. **More Sophisticated Treatment Strategies**: The proposed method discretizes the dosages of Intravenous (IV) and Vasopressor treatments into 10,000 categories, allowing an extensive exploration of the potential treatment combinations. This approach provides a broader range of treatment options when compared to those of traditional RL, which often involves a limited set of predefined actions.