## 6 Hybrid Ascending Feature Selection with Layer-Static-Weighted Attention Algorithm (HAFS-LWA)  
As presented in Figure 8, the proposed HAFS-LWA flowchart aims to find the best model for predicting adverse drug events using the FAERS dataset. First, data are preprocessed by tokenizing and encoding text data and one-hot encoding categorical data. Then, features are selected by ranking them based on their importance. Next, the model is trained iteratively, selecting the top features and performing cross-validation at each step. The algorithm uses layer-static-weighted attention to dynamically adjust the focus between natural language processing and demographic features. Finally, the model with the best average performance across validation folds is selected and evaluated on test data. This systematic approach helps to optimize the model's performance. Our proposed methodological landscape, crafted with a blend of advanced techniques and iterative refinement, was sculpted to derive the most meaningful and actionable insights from the FAERS dataset. This holistic approach, encompassing data preprocessing to model interpretation, lays a robust foundation for any future research endeavors in the domain of drug-related adverse events. Figure 9 explains three separate NLP models, each with 4000 features. Each NLP model then passes through three dense layers, which are artificial neural network layers that take in a set of inputs and produce a set of outputs. The number of features in each dense layer decreases through the network. For example, the first dense layer in each model has 512 features, the second dense layer has 128 features, and the third dense layer has 13 features. After the third dense layer, each model splits into two branches. One branch goes to a \"demographic features\" block, which contains some additional features that are specific to the task. The other branch goes to an \"attention weight\" block. The attention weight block calculates a weight for each of the 64 features in the input. This weight represents how important that feature is for the task. The weights are then used to combine the features from the \"NLP\" and \"demographic features\" blocks into a single vector of 64 features. Initially, the attention distribution was set with a ratio of \(\alpha=0.1\) for NLP features and \(1-\alpha=0.9\) for demographic features. We mathematically modeled this attention allocation through a performance function \(P(\alpha)\), which was rigorously designed to quantify the model's classification accuracy. To ensure an exhaustive search for the optimal feature balance, we iteratively adjusted \(\alpha\) in increment of 0.1, systematically exploring the performance landscape as explained in the following equations:  
\[\alpha i+1=\alpha i+0.1 \tag{1}\]  
\[P(\alpha i+1)>P(\alpha i) \tag{2}\]  
This iterative process is the crux of our optimization methodology, continuing until the improvement in \(P\) plateaus, indicating the achievement of the optimal attention ratio. It is crucial to note that this methodical adjustment of \(\alpha\) is not merely a linear search; it is a strategic exploration that accounts for non-linear interactions between features. The optimal \(\alpha\) captures the most effective distribution of attention, fine-tuning the model to exploit the intricate patterns and dependencies within the data. Our layer-static-weighted attention mechanism is a significant departure from traditional static weighting schemes. By dynamically reallocating attention, our model adaptively learns which features yield the most significant predictive power. This is particularly beneficial in the context of adverse drug event reporting, where the interplay between the textual descriptions of ADRs and demographic information can be complex and highly variable. Moreover, the empirical results obtained through this innovative approach demonstrate superior performance compared to static models.