Layer-Weighted Attention and Ascending Feature Selection: An Approach for Seriousness Level Prediction Using the FDA Adverse Event Reporting System

In this study, we introduce a novel combination of layer-static-weighted attention and
ascending feature selection techniques to predict the seriousness level of adverse drug events using
the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We utilized
natural language processing (NLP) to analyze the terms in the active substance field, in addition to
considering demographic and event information such as patient sex, healthcare provider qualification,
and drug characterization. Our ascending feature selection method, which progressively incorporates
additional features based on their importance, demonstrated continuous enhancements in prediction
performance. Simultaneously, we employed a layer-static-weighted attention technique, which
dynamically adjusts the model’s focus between natural language processing (NLP) and demographic
features. This technique achieved its best performance at a balanced weight of 50%, yielding an
average test accuracy of 74.56% and CV ROC score of 0.83 when 4000 features were included,
indicating a compelling advantage to include a larger volume of meaningful features. By integrating
these methodologies, we constructed a robust model capable of effectively predicting seriousness
levels, offering significant potential for improving pharmacovigilance and enhancing drug safety
monitoring. The results underscore the value of NLP and demographic data in predicting drug event
seriousness and demonstrate the effectiveness of our combined techniques. We encourage further
research to refine these methods and evaluate their application to other clinical datasets.