Predictive Modeling of Adverse Event Severity and Reoccurrence in Pharmaceutical Drug Administration

Forecasting adverse events (AEs) in drug delivery remains one of the most challenging tasks in personalized medicine and patient safety management. We will explore the high potentiality of ML algorithms to estimate the severity and recurrence of AEs using a complete patient-data set consisting of 5,400 records across 12 clinical and pharmacological features. Four different classifiers are compared for their predictive efficiency: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Classifier with a Radial Basis Function kernel. The experiments showed perfect predictive results, with Gradient Boosting showing stellar predictive accuracy in the domain of severity at the incredible rate of 98.15 %, and Logistic Regression reaching predictive accuracy in the reoccurrence domain at the high rate of 94.72 %. To address the existing gaps in literature, the current research included multi-dimensional patient profile characteristics such as genomic risk factors, drug interaction profiles, and organ functional biomarkers in an integrative set of predictive models. The feature importance analysis revealed the history of adverse events, liver-functioning markers, and drug dosage as the most successful predictors. The outcome points out the potentiality of machine learning-powered systems in enhancing the process of clinical decision-making, thereby allowing proactive intervention policies and enhancing therapeutic safety mechanisms in advanced healthcare practices.