## 2 Related Works
Table 1 provides an overview of various research studies that aim to address different objectives related to the prediction and assessment of adverse drug events (ADEs) and drug interactions. The common theme among these objectives is the use of diverse datasets and machine learning or analytical techniques to improve our understanding of ADEs and related factors. \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Ref.** & **Objective** & **Applied** & **Applied Mechanism** & **Performance** \\ \hline \multirow{4}{*}{[9]} & Identify the medications that are most likely to have adverse effects & FAERS dataset & Applying information components with a pharmacological network model (PNM) for predicting adverse drug events & AUROC = 0.82 \\  & Develop a network-based method for predicting relationships between drugs and ADEs & FAERS dataset & – & Pharmacological network model (PNM) \\  & Develop a network-based method for predicting relationships between drugs and ADEs & FAERS dataset & – & Pharmacological network model (PNM) \\  & Develop a network-based method for predicting relations between drugs and ADEs & FAERS dataset & – & Similarity measure \\  & Apply a new method by using & & & \\  & Electronic health records for predicting drug side effects & EHR corpus & RF algorithm & AUC = 0.92 \\  & Compare different methods to use & & & \\  & electronic health records for predicting drug side effects using the RF & EHR corpus & RF algorithm & Mean AUC = 4.3 \\  & algorithm & & & \\  & Identify patients at high risk of & & & \\  & developing renal adverse events using & Dataset from different clinics & RF–kNN–xGB–SVM–NB–LR & AUC = 0.98 \\  & ML algorithms & & & \\  & Develop and evaluate deep learning & & & \\  & models for ADE prediction and assess & Large EPR dataset & Deep learning models for improving predictions & AUROC = 0.774 \\  & the relevance of attention mechanisms & & & \\  & in improving prediction & & & \\  & Investigate how well various machine & & & \\  & learning algorithms perform in & FAERS & Machine learning regression models & Mean square error of 152.72 \(\pm\) 11.17 \\  & predicting ADE onset times and help & Dataset & & \\  & in selecting the right models for their & & & \\  & unique use cases & & & \\  & Assess the effectiveness of & & & CADEC \\  & transformer-based models and & CADEC & & precision = 0.5789 \\  & investigate the effects of adding & & & \\  & further processing layers like CRF and & & datasets & \\  & LSTM & & & \\  & Identify potential risks of ADEs, & & & \\  & which are adverse reactions and side & FAERS & Weighted score to select ADE associations & NA \\  & effects that can occur when people & dataset & & \\  & take drugs & & & \\  & Apply two drug characteristics to & & & \\  & comprehend and model adverse & FAERS & Multi-task multi-attribute with & AUC = 0.9247 \\  & interactions among drugs and then & & & \\  & capture the interactions & & & \\  & Identify and evaluate the relative risk & & & \\  & of medications that cause itching & FAERS & & \\  & based on the place of itching onset & dataset & & \\  & and mode of administration & & & \\  & Analyze vaccine adverse event data & VAERSDATA & – & Logistic regression & LR ROC = 0.65 \\  & using ontologies and ML & VAERSYMPTOMS & – & XGBoost & XGBoost ROC = 0.69 \\  & & VAERSVAX & & & \\  & Predict drug–drug interactions with & & & \\  & histamine antagonists using ML & DrugBank dataset & NB–LR–DT–RF–XGBoost & DT ROC = 0.966 \\  & algorithms & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of several adverse drug reactions with their performance. The application of ascending feature selection, as proposed in our work, has been relatively unexplored, providing an innovative means to gradually increase model complexity and understand how model performance varies accordingly. Moreover, while attention mechanisms have been widely used in the NLP field, their application in weighing the importance of different types of features in a dataset has been limited.