---
language:
- en
license: cc0-1.0
task_categories:
- text-generation
tags:
- medical
- clinical
- adverse-events
- instruction-tuning
- alpaca
pretty_name: Adverse Event Symptom Narrative Generation Dataset
size_categories:
- n>1M
---

# Adverse Event Symptom Narrative Generation Dataset

## Dataset description

A dataset of VAERS symptom narratives and diagnostic (laboratory) findings transformed into instruction-tuning format (Alpaca) to finetune conditional and unconditional generative models.

This dataset contains 2,649,322 training examples derived from VAERS (Vaccine Adverse Event Reporting System) data for the years 1990-2025.

The data has been transformed into instruction-tuning format (Alpaca) suitable for fine-tuning language models on clinical adverse event reporting.

### Dataset summary

- **Task**: Clinical text generation from structured patient and vaccine information
- **Language**: English
- **Output field**: SYMPTOM_TEXT
- **Patient history included**: No
- **Format**: Alpaca instruction-tuning format
- **Years covered**: 1990-2025
- **Total examples**: 2,649,322

### Key features

- **Automatic deduplication**: One training example per VAERS_ID (consolidates multiple vaccines per patient)
- **Manufacturer exclusion**: Vaccine manufacturers excluded to avoid brand bias
- **List preservation**: Vaccines and symptoms preserved as lists for multiple-value fields
- **Quality filtering**: Records with empty output fields excluded

## Data structure

Each example contains three fields following the Alpaca format:

- **instruction**: Task description for the model
- **input**: Structured patient and vaccine information including:
  - Age (years)
  - Sex
  - Vaccine(s) administered (multiple vaccines joined with commas)
  - Symptoms (multiple symptoms joined with commas, MedDRA versions filtered)
  - Patient history (optional, if `include_history=True`)
- **output**: Clinical narrative text from VAERS reports

### Data fields

| Field | Type | Description |
|-------|------|-------------|
| instruction | string | Task instruction for the model |
| input | string | Structured patient/vaccine information (newline-separated) |
| output | string | Clinical narrative (SYMPTOM_TEXT) |

### Internal data representation

While the published dataset uses Alpaca format (string fields), the internal processing preserves structured data:

- `vaccines_list`: List of vaccine types (e.g., ["COVID19", "FLU"])
- `symptoms_list`: List of symptoms (e.g., ["Headache", "Fatigue", "Myalgia"])
- `manufacturers_list`: List of manufacturers (excluded from training data)
- `dose_series_list`: List of dose numbers

### Example Record

```json
{
  "instruction": "Generate a clinical symptom description based on the patient and vaccine information provided",
  "input": "Age: 0.2\nSex: F\nVaccine: DTP\nSymptoms: Agitation",
  "output": "Loud intense cry with screaming for 1 1/2 hrs.  Seen next day, child  normal."
}
```


## Source data

This dataset is derived from the CDC VAERS (Vaccine Adverse Event Reporting System) public data, available at:
https://vaers.hhs.gov/data.html

VAERS is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. The system is co-managed by the CDC and FDA.

### Data processing

The raw VAERS data consists of three CSV files per year:
- VAERSDATA: Patient demographics and clinical narratives
- VAERSVAX: Vaccine administration details
- VAERSSYMPTOMS: Coded symptoms using MedDRA terminology

Processing steps:
1. **Loading**: CSV files loaded with automatic encoding detection (supports UTF-8, Latin-1, Windows-1252)
2. **Joining**: Tables joined by VAERS_ID
3. **Deduplication**: Multiple vaccines per patient consolidated into single training example
4. **Filtering**: MedDRA version numbers removed, empty outputs excluded, manufacturers excluded
5. **Transformation**: Converted to Alpaca instruction-tuning format

## Intended use

### Primary use cases

- Fine-tuning language models for clinical adverse event reporting
- Training models to generate clinical narratives from structured patient data
- Research in medical natural language generation
- Development of clinical documentation assistance tools

### Out-of-scope use

- Clinical decision-making without expert oversight
- Automated diagnosis or treatment recommendations
- Replacement for professional medical judgment

## Limitations and biases

### Data limitations

- **Reporting bias**: VAERS is a passive surveillance system; not all adverse events are reported
- **Causality**: VAERS reports do not establish causal relationships between vaccines and adverse events
- **Completeness**: Not all fields are complete in every report
- **Temporal coverage**: Dataset covers years 1990-2025

### Potential biases

- **Reporting patterns**: Healthcare providers and vaccine manufacturers are required to report certain events, while patient reporting is voluntary
- **Media influence**: Increased reporting following media coverage of vaccine-related events
- **Temporal bias**: Reporting practices and data quality have evolved over time

## Ethical considerations

- This dataset contains information about adverse events following vaccination
- Reports in VAERS do not establish causation
- Models trained on this data should not be used for medical decision-making without appropriate expert oversight
- Users should be aware of the limitations of passive surveillance data

## License

This dataset is derived from U.S. government public data and is available under the Creative Commons Zero (CC0) license.

## Citation

If you use this dataset, please cite the original VAERS data source:

```
Vaccine Adverse Event Reporting System (VAERS)
Centers for Disease Control and Prevention (CDC) and Food and Drug Administration (FDA)
Available at: https://vaers.hhs.gov/data.html
```

## Dataset Creation

**Created**: 2025-11-10

**Tool**: VAERS Fine-Tuning Preprocessor
**Repository**: https://github.com/chrisvoncsefalvay/vaers-ft-preprocessor

**Processing configuration**:
- Output field: SYMPTOM_TEXT
- Include history: No
- Years processed: 1990-2025
- Total examples: 2,649,322