## 2 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**1 st Day after**} & \multicolumn{2}{c}{**4 th Day after**} & \multicolumn{2}{c}{**7 th Day after**} \\  & \multicolumn{2}{c}{**Surgery**} & \multicolumn{2}{c}{**Surgery**} & \multicolumn{2}{c}{**Surgery**} \\ \cline{2-7}  & **MAPE** & \begin{tabular}{c} **Gain\_** \\ **MAPE** \\ \end{tabular} & **MAPE** & \begin{tabular}{c} **Gain\_** \\ **MAPE** \\ \end{tabular} & **MAPE** & \begin{tabular}{c} **Gain\_** \\ **MAPE** \\ \end{tabular} \\ \hline Our meta-model & **19.36 \(\pm\) 1.11** & & **22.16 \(\pm\) 2.79** & & **14.40 \(\pm\) 1.96** & \\ Functional & 80.32 \(\pm\) 6.00 & 4.15 & 58.14 \(\pm\) 6.20 & 2.62 & 35.64 \(\pm\) 2.81 & 2.47 \\ Juhl-Frost & 66.43 \(\pm\) 4.20 & 3.43 & 46.42 \(\pm\) 4.72 & 2.09 & 27.47 \(\pm\) 3.60 & 1.91 \\ [PERSON] & 76.28 \(\pm\) 5.46 & 3.94 & 54.59 \(\pm\) 5.33 & 2.46 & 32.58 \(\pm\) 3.06 & 2.62 \\ Average gain & & 3.84 & & 2.39 & & 2.21 \\ \hline  & \multicolumn{2}{c}{**1 st Day after**} & \multicolumn{2}{c}{**4 th Day after**} & \multicolumn{2}{c}{**7 th Day after**} \\  & \multicolumn{2}{c}{**Surgery**} & \multicolumn{2}{c}{**Surgery**} & \multicolumn{2}{c}{**Surgery**} \\ \cline{2-7}  & **MAE** & \begin{tabular}{c} **Gain\_** \\ **MAE** \\ \end{tabular} & **MAE** & \
\begin{tabular}{c} **Gain\_** \\ **MAE** \\ \end{tabular} & **MAE** &
\begin{tabular}{c} **Gain\_** \\ **MAE** \\ \end{tabular} \\ \hline Our meta-model & **8.24 \(\pm\) 0.93** & & **10.56 \(\pm\) 0.87** & & **7.98 \(\pm\) 1.51** & \\ Functional & 31.30 \(\pm\) 2.44 & 3.80 & 25.40 \(\pm\) 2.51 & 2.41 & 18.86 \(\pm\) 1.01 & 2.36 \\ Juhl-Frost & 25.87 \(\pm\) 1.79 & 3.14 & 20.03 \(\pm\) 2.32 & 1.90 & 14.32 \(\pm\) 1.64 & 1.79 \\ Nakahara & 29.57 \(\pm\) 2.06 & 3.59 & 23.65 \(\pm\) 2.40 & 2.34 & 17.11 \(\pm\) 1.25 & 2.14 \\ Average gain & & 3.51 & & 2.18 & & 2.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performances of the proposed meta-model, along with performances of three standard models (Functional segments, Juhl-Frost, Nakahara). Figure 4: Residuals for the fourth postoperative day prediction. Figure 3: Residuals for the first postoperative day prediction. ## 4 Model Interpretation

Model interpretation is essential in the field of medicine to ensure patient safety, support clinical decision-making, identify biases, improve model performance, and foster trust between ML systems and healthcare professionals. It is a key component for the safe and effective application of the full ML potential in improving healthcare practices [38; 39; 40]. In order to demonstrate the effectiveness of the latest agnostic methods and tools for interpreting complex ML models, we opted for SHAP due to its good theoretical and practical properties. It is based on cooperative game theory by assigning an importance value for every sample to each feature [11]. In this context, agnostic means that SHAP can be applied to any black-box ML model without requiring knowledge of its internal architecture or parameters. These properties allow SHAP to have both local and global levels of model interpretation. Global explanation aims to provide insights into the overall behavior and trends of a ML model across the entire dataset. It highlights the general relationships between input features and model predictions, revealing which features tend to have more significant impacts on predictions on average. On the other hand, local explanation focuses on explaining the prediction made by the model for a specific instance or observation. It provides insights into why the model arrived at a particular decision for that individual case. All experimental results were obtained using the package described in [41]. The procedure for calculating SHAP values for a given meta-model is presented in Algorithm 1. In Step 3, the algorithm returns SHAP values for each feature and each observation in the training data set. In Step 4, the algorithm calculates an average of SHAP values over the entire training data set for each feature.