FAERS GLP-1 Adverse Drug Effect Analysis
Machine learning experiment — identifying adverse drug event signals for GLP-1 receptor agonists from the FDA Adverse Event Reporting System (FAERS)
Problem
Section titled “Problem”GLP-1 receptor agonists (Ozempic, Wegovy, Mounjaro, etc.) have seen explosive adoption for diabetes and weight loss. The FDA Adverse Event Reporting System (FAERS) contains millions of voluntary adverse event reports — but the data is noisy, inconsistent and requires signal detection to separate real pharmacovigilance signals from reporting artifacts.
The goal of this experiment is to apply machine learning to identify and characterize adverse drug event patterns specific to the GLP-1 drug category, going beyond what standard disproportionality analysis surfaces.
Approach
Section titled “Approach”(Detailed content to follow — methodology, feature engineering, signal detection approach, and model selection.)
Dataset
Section titled “Dataset”- Source: FDA FAERS (Adverse Event Reporting System)
- Drug category: GLP-1 receptor agonists
- (Data scope, extraction method, and preprocessing details to follow.)
Tech stack
Section titled “Tech stack”(To follow — Python ML stack, data processing pipeline, visualization.)
Results & findings
Section titled “Results & findings”(To follow — after experiment completion.)
What I am learning
Section titled “What I am learning”This experiment explores the intersection of pharmacovigilance and machine learning — applying data science and ML techniques to a real-world public health dataset to identify adverse drug event signals that matter.