Skip to content

Analyzed FAERS data to detect and visualize Adverse Drug Reaction (ADR) signals using Python and Power BI. Identified drug–reaction patterns, monitored safety trends, and generated insights to support pharmacovigilance and patient safety decisions.

License

Notifications You must be signed in to change notification settings

sntsh-code/ADR-Signal-Detection-Trend-Analysis

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

29 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

*\

===================================== ADR Signal Detection & Trend Analysis


Welcome to ADR Signal Detection & Trend Analysis Project repository!

This project is based on the original FAERS (FDA Adverse Event Reporting System) dataset of Q2 2025, a real-world source of adverse drug reaction (ADR) reports submitted to the FDA. The dataset has been curated and cleaned to support educational and analytical purposes, enabling signal detection, trend analysis, and data-driven insights in pharmacovigilance.


Project Overview

ADR Signal Detection & Trend Analysis is a pharmacovigilance project leveraging the real-world FAERS (FDA Adverse Event Reporting System) dataset. The goal is to identify adverse drug reactions (ADRs), detect potential safety signals, and analyze trends across drugs, patients, and regions.


Objectives

The main objectives of this project are: ⦁ Process the raw FAERS data to remove duplicates, handle missing values, and standardize formats. ⦁ Ensure referential integrity between datasets for accurate analysis. ⦁ Adverse Drug Reaction (ADR) Signal Detection ⦁ Detect potential safety signals using statistical measures and trend analysis. ⦁ Analyze ADR occurrences over time, by drug, by patient demographics, and by region. ⦁ Apply real-world pharmacovigilance concepts using Python, SQL, and Power BI. ⦁ Develop a reproducible workflow suitable for portfolio projects and professional demonstration.


Expected Outcomes

Upon completing this project, the following outcomes are expected:

⦁ Identify drugs associated with frequent or serious adverse drug reactions (ADRs). ⦁ Detect trends and patterns in ADR reporting over time. ⦁ Analyze patient demographics to find high-risk groups. ⦁ Create interactive dashboards and visual reports for clear insights. ⦁ Develop a reproducible analytical workflow for pharmacovigilance projects.


Key Findings

⦁ Drug like HUMIRA is having the most ADR Reports. ⦁ USA has the most ADR cases with 4835 total active cases. ⦁ Month of MAY reported the most numbers of ADR cases in 2025 Q2. ⦁ Drug named NUOLAZID show DEATH as reaction in 36 cases. ⦁ Age 60 is most highest rated age foe ADR reactions. ⦁ FEMALES shows most number of ADR cases as overall comparison.


FAERS ADR Analysis Report Q2 2025

FAERS ADR Analysis Insight Report 2025 provides a comprehensive examination of adverse drug reactions using the FDA’s FAERS dataset. It identifies high-risk drugs, serious versus non-serious cases, and key drug-reaction patterns. Temporal trends and patient demographics are analyzed to reveal emerging safety signals. The report delivers actionable insights for pharmacovigilance, regulatory monitoring, and patient safety.


Recommendations

⦁ Focus monitoring on high-risk drugs like HUMIRA with the most ADR reports. ⦁ Implement enhanced surveillance in the USA, which has the highest number of ADR cases. ⦁ Investigate the peak in ADRs during May 2025 to understand seasonal or reporting trends. ⦁ Review severe reactions from drugs like NUOLAZID, especially cases resulting in death. ⦁ Pay special attention to patients aged 60+ and females, who show higher ADR incidence.


License

This project is licensed under the MIT License You are free to use, modify, and share this project with proper attribution.


Disclaimer

This dataset is a real-world dataset derived from the FDA Adverse Event Reporting System (FAERS), publicly available on the FDA website. While processed for analysis, it contains actual adverse event reports submitted to the FDA.


About Me

Hi, I’m Santosh – Healthcare Data Analytics and Pharmacovigilance specialist with expertise in drug safety, data analytics, and adverse event reporting. I work at the intersection of healthcare and data, using Python, SQL, and visualization tools to generate actionable insights that improve patient safety. I’m passionate about pharmacovigilance, regulatory compliance, and building data-driven healthcare projects.

About

Analyzed FAERS data to detect and visualize Adverse Drug Reaction (ADR) signals using Python and Power BI. Identified drug–reaction patterns, monitored safety trends, and generated insights to support pharmacovigilance and patient safety decisions.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages