DS RPC 2.0 : ADEGuard – AI-Powered Adverse Drug Event Detection and Severity Mapping

This is the starter repository for Codebasics's Resume Project Challenge 2.0.

This project focuses on building an AI-powered pipeline to detect Adverse Drug Events (ADEs) from symptom text, group them into symptom-based and age-specific clusters, and classify each event by severity.

Please fork this repository to get started.

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📂 Data Access Instructions

Contestants will use the VAERS dataset provided by the U.S. Vaccine Adverse Event Reporting System.

1. Visit the official VAERS Data page:
   👉 https://vaers.hhs.gov/data/datasets.html

2. Scroll to the table listing data by year.

3. Download the ZIP file for your target year(s) from the "Zip File" column.

   • Example: For 2025, click the link in the Zip File column (e.g., 4.95 MB).
   • The ZIP will contain three CSV files:
     • VAERSDATA.csv → Main case and patient data
     • VAERSSYMPTOMS.csv → Coded adverse event terms using the MedDRA (Medical Dictionary for Regulatory Activities) terminology.
       • Each report can have up to five coded symptoms (SYMPTOM1–SYMPTOM5), representing standardized MedDRA Preferred Terms.
     • VAERSVAX.csv → Vaccine/product details

4. Extract the ZIP files for all target years, and move all three CSV files from each ZIP into the data/raw folder of this repository.

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📝 Annotation Guidelines

Before starting annotation or model training, review the Annotation Guidelines in the docs/ folder.
They explain in detail:

• ADE annotation rules – how to identify Adverse Drug Events in text, including what to include and what to skip.
• DRUG annotation rules – how to label vaccine or drug mentions exactly as reported, handle brand names, code names, and generic terms.
• Special cases – rules for compound symptoms, repeated mentions, death/hospitalization references, and COVID-19 mentions.
• Span formatting – keeping longest medically accurate terms, excluding durations, and labeling each occurrence separately.
• Quick checklist – a step-by-step reminder to ensure annotations are consistent and compliant.

📌 Tip: Following these rules strictly ensures the labels are high quality and consistent, which is critical for training the NER model effectively.

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📌 Learn More

Visit the challenge page to learn more: DS RPC-2.0