Automating Adverse Event Detection in Clinical Trials Through Machine Learning

Abstract Adverse drug events (ADEs) in clinical trials present significant challenges, as delayed or inaccurate detection can compromise patient safety and trial outcomes. This research focuses on automating the detection of ADEs using machine learning models, leveraging the FDA Adverse Event Reporting System (FAERS) dataset. The goal is to improve the speed and accuracy of ADE identification by developing a robust predictive model. The FAERS dataset, consisting of millions of records, was preprocessed to address missing values, outliers, and inconsistencies. Key variables, such as patient demographics, drug characteristics, and trial-specific parameters, were identified and used to train machine learning models, including logistic regression, random forests, and XGBoost. XGBoost, in particular, demonstrated superior performance with an accuracy of 66.09% and a precision of 0.78, highlighting its effectiveness in handling complex, high-dimensional data. Data preprocessing techniques, such as normalization, encoding, and feature engineering, were essential to improving model performance. A real-time prediction tool was also developed using Streamlit, enabling healthcare professionals to predict ADE outcomes based on input features. This study concludes that machine learning models, particularly XGBoost, offer significant potential for improving ADE detection in clinical trials, reducing reliance on manual reporting, and enhancing drug safety monitoring. Future work will focus on expanding the dataset, incorporating real-time data streams, and refining the predictive tool for broader application in pharmacovigilance systems (Bhatt et al., 2020; Mysore et al., 2019). Keywords: Adverse Drug Events (ADEs), Machine Learning, FDA Adverse Event Reporting System (FAERS), Clinical Trials, Pharmacovigilance, XGBoost, Predictive Modeling.