## 2 Related Works  
The field of pharmacovigilance has experienced remarkable advancements with the advent of machine learning and natural language processing (NLP). Several studies have proposed different methodologies to harness the rich information present in adverse event reporting databases like the FAERS. The application of NLP in adverse event detection and classification has been a prominent trend. For instance, the authors of [5] developed an approach using NLP and machine learning to automatically classify clinically relevant drug-related entities from medical case reports. However, these approaches predominantly focus on NLP features, often neglecting the value of other structured data present in such databases. The authors of [6] introduced a decision tree-based stratification approach that takes into account both patient and drug-related variables. This method excelled in signal detection compared to alternative methods, highlighting its efficacy in tackling data masking concerns and improving the precision of adverse drug reaction detection. The authors of [7] explained the difficulty healthcare organizations face in predicting and preventing adverse events (AEs), which can result in severe harm or even death. While the United States has established systems like the FDA and FARES to track AEs, many countries, notably in the Middle East, lack such infrastructure. In [8], the authors aimed to improve patient safety by addressing the issue of drug-drug interactions (DDIs). Their study aimed to reduce alert fatigue in healthcare settings by developing a machine learning framework to identify high-priority DDIs by extracting information from FDA adverse event reports. The method uses stacked autoencoders, weighted support vector machines, and an autoencoder-based semi-supervised learning algorithm to improve classification performance, outperforming other approaches. In [9], the authors enhanced the pharmacovigilance's ability to predict ADEs. They applied an information component-guided pharmacological network model (IC-PNM) as a novel approach to test the data from the FDA Adverse Event Reporting System (FAERS). To increase the precision of medication ADE predictions, the IC-PNM combines the advantages of two approaches. The researchers developed a method to predict which drugs are more likely to cause side effects. In [10], a new model was applied to identify medications that are more likely to result in side effects. Data were extracted from a bipartite network, where nodes stand for drugs and ADEs, and edges represent their connections. Their study aims to demonstrate the superior predictive capabilities using different ML models. The difficulty of predicting adverse drug reactions (ADEs) using electronic health records (EHRs) was presented in [11]. EHRs are frequently too large and complex for conventional analysis techniques. Therefore, the dimensionality of the EHR data is reduced by using random indexing. Next, the authors evaluated the effect of random indexing on prediction accuracy by predicting ADEs using the RF model. Another method for detecting adverse drug events was presented in [12]. The paper reported on a set of experiments that were carried out using clinical measurements from EHRs to identify patients with adverse drug events (ADEs). The accuracy and AUC of the predictive models the researchers created were assessed using the random forest algorithm. Initially, the researchers contrasted various approaches to clinical measurement representation in the EHR data. The significance of each representation for ADE prediction was then investigated. The authors contrasted various classifiers to determine which performed the best across the various representations. The study in [13] aimed to use ML algorithms to identify risk factors for renal adverse events caused by renin-angiotensin-aldosterone system inhibitor (RAASI) medications.