A Comparison of Methods of Data Mining Algorithms Directed Predictive Pharmacosafety Networks for Adverse Drug Event Detection

Ji, Xiangmin; Hua, Liyan; Zhang, Yunfei; Li, Jin

doi:10.1109/icma.2019.8816543

Cited by 2 publications

(1 citation statement)

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“…As proposed in [24], different methods of ML algorithms were investigated in a comprehensive review to predict the networks in pharmacosafety where the FAERS dataset was applied. The main objective was to predict adverse drug events by splitting the dataset based on the interaction between drugs and ADEs.…”

Section: Proposed Methodologymentioning

confidence: 99%

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

Aldughayfiq,

Allahem,

Mostafa

et al. 2024

Applied Sciences

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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.

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Section: Proposed Methodologymentioning

confidence: 99%