## 4 Discussion  
In this paper, three distinct methods for selecting the best LSTM model architectures have been investigated for the estimation of MAP during anesthesia: population-specific, patient-specific, and category-specific. The population-specific method employed a uniform architecture for all patients; the patient-specific method fine-tuned the architecture for each individual; and the category-specific method used five distinct architectures based on the correlation categories between inputs (CeProp and CeRemi) and the output (MAP). The patient-specific method arises as the best architecture, with the lowest MAE and MSE, and the highest \(R^{2}\). However, this method was also the most computationally intensive, taking 130 h to find the optimal architecture for each patient. The population-specific method was the most computationally efficient (i.g. 1 h). However, it showed the worst estimation accuracy, with the highest MAE and MSE and the lowest \(R^{2}\). The category-specific method can be seen as a trade-off solution between the patient-specific and population-specific methods, with a computational time of 53 h and an estimation performance close to the patient-specific method. This investigation indicates the pivotal role of architectural selection in deep learning models, as they significantly influence model performance. For LSTM models, these architectural parameters (e.g., the optimizer, the number of layers) had a significant impact on the ability of the model to learn from the data and make accurate estimations. Exploring the best LSTM architectures for MAP estimation has significant implications in clinical settings. By improving the accuracy of MAP forecasting every 5 s, models can enhance individualized patient care, allowing anesthesiologists to make more informed and timely decisions regarding drug administration. This could lead to better maintenance of hemodynamic stability during surgery, reducing the risk of complications such as hypotension or hypertension. The evaluation of LSTM architecture has been extensively explored in the literature, with various approaches employed to improve predictive performance. Several studies have compared different optimizers, e.g., Adam, SGD, RMSprop, and Adagrad, to deter \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Time Consumed to Find the Architecture** \\ \hline Population-specific & Less than 1 h \\ \hline Patient-specific & 130 h \\ \hline Category-specific & 53 h \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total time consumed to find the LSTM model architectures for the three methods. mine their impact on LSTM model performance [30; 31]. Other investigations have focused on the selection of activation functions, with sigmoid and tanh functions being widely evaluated [32]. Additionally, research has been conducted on the choice of the number of hidden layers and nodes within each layer, aiming to optimize model complexity and predictive accuracy [33; 34]. Previous studies focused on optimizing hyperparameters within a fixed architecture, whereas this study proposes a novel method that tailors the architecture to individual patients and input-output correlation categories. Our finding also highlights the significant effect of the correlation between inputs (CeProp and CeRemi) and the output (MAP) on LSTM model performance. Patients with a high correlation between inputs and output experienced accurate estimations, contrasting with patients with a low correlation, who showed worse predictive performance among the three methods. This reveals the importance of considering the correlation in implementing AI for prediction purposes. This necessitates the imperative of personalized healthcare in the realm of anesthesia, as responses to anesthetic drugs can differ significantly from one individual to another.