## 2 Related Works
The researchers will use these findingsto develop predictive models that can be used to identify patients at high risk of developing these adverse events so that they can be monitored and treated more closely. According to [14], using electronic patient record (EPR) data, deep learning models can predict ADEs with a high degree of accuracy. Two cutting-edge medical code-based models performed noticeably worse than the top-performing model. The study also discovered that deep learning models for ADE prediction could be made more interpretable by utilizing attention mechanisms. The model was able to determine which features in the EPR data were most crucial for ADE prediction thanks to the attention mechanism. Clinicians can utilize this information to create more successful preventative plans and gain a better understanding of how ADEs occur. Furthermore, the authors of [15] highlighted the importance of using interactive support tools to reduce the number of ADEs and prevent unnecessary prescribing cascades in older adults. Instead of just predicting whether ADEs will occur in cancer patients, the authors of [16] attempted to develop a new method for timing ADEs. A range of machine learning algorithms, including both conventional and cutting-edge methods, were used in the study to evaluate how well these algorithms predict the onset of ADEs in these patients. Graph-based embedding is a novel technique employed in this study that has not been used to predict ADE onset times before. In addition, the authors of [17] discussed the importance of extracting ADEs from informal text data, such as tweets and forum posts, for digital pharmacovigilance. They emphasized the growing use of large pretrained language models, such as BERT, for this task, but acknowledged that it is unclear which transformer-based models are the most effective. In [18], the authors introduced a new ADE association network that uses a large dataset of known drug-ADE associations and a scoring system to predict new drug-ADE combinations. The study shows that the network works well by both finding known drug-ADE relationships and predicting new ones, which shows that this comprehensive toxicological approach is valuable. A novel ML model named MTMA is presented in [19]. It has the potential to forecast drug interactions that may result in unfavorable outcomes. Two aspects of drugs are taken into account by MTMA: adverse effects and molecular structure. It can aid in our understanding of how drug interactions function by capturing the interactions between medications using a unique kind of tensor. The authors of [20] performed a comprehensive study about the use of the FAERS database, which has adverse event reports from different parts of the world, to assess drug-induced itching. It is important to note that this study is the first in-depth attempt to identify and evaluate the relative risks of medications that induce itching. In addition, a novel classification scheme based on the place of itch onset and mode of administration was presented in the study, which may help to clarify the mechanisms underlying drug-induced itching. Another research methodology regarding the measurement of the adverse effects of the COVID-19 vaccine is presented in [21]. The primary objective of this work was to analyze vaccine adverse event data using ontologies and ML. To make it simple for users to access and obtain these data, the researchers also created an intuitive user interface. Lastly, the researchers employed machine learning to forecast the main symptoms linked to post-vaccination hospitalization and therapy. This study sheds important light on the side effects and safety of vaccines. Furthermore, the authors of [22] created a novel ML model that can precisely forecast drug-drug interactions, particularly those involving histamine antagonists. The early stages of drug development can benefit from the early application of this model, which outperforms earlier models and has the potential to increase medication safety and streamline the process.