louisbrulenaudet/clinical-trials

Clinical Trials Dataset

A comprehensive dataset of clinical trials sourced from ClinicalTrials.gov, featuring structured metadata, detailed study information, and pre-computed semantic embeddings for machine learning applications in biomedical research.

Dataset Description

This dataset provides a rich collection of clinical trial information systematically collected from the official ClinicalTrials.gov database. Each record contains detailed study metadata, eligibility criteria, intervention descriptions, outcome measures, and organizational information. The dataset is enhanced with semantic embeddings generated using specialized biomedical language models, making it immediately ready for downstream ML tasks.

Key Features

• Comprehensive Coverage: Complete clinical trial records with 30+ structured fields
• Rich Metadata: Study phases, enrollment data, eligibility criteria, and outcome measures
• Temporal Data: Complete timeline information from submission to completion
• Medical Ontology: MeSH terms and condition/intervention classifications

Dataset Statistics

Metric	Value
Total Studies	500,000+
Data Source	ClinicalTrials.gov Official API
Languages	English

Data Collection Methodology

The dataset is built using a robust data pipeline that ensures data quality and consistency:

1. API Integration: Direct connection to ClinicalTrials.gov API v2
2. Data Validation: Pydantic models ensure schema compliance and data integrity
3. Type Conversion: Automatic parsing of dates, numbers, and JSON structures
4. Embedding Generation: Semantic embeddings computed using domain-specific models

Data Quality Assurance

• Schema Validation: All records validated against comprehensive Pydantic models
• Type Safety: Proper data type conversion with null handling
• Deduplication: Unique constraint on NCT ID prevents duplicates
• Temporal Consistency: Date validation and chronological ordering
• Text Normalization: Whitespace cleanup and encoding standardization

Schema Overview

Core Study Information

• nct_id: Unique study identifier (NCT########)
• brief_title: Concise study title
• official_title: Complete formal study title
• study_type: Study design type (Interventional, Observational, etc.)
• phases: Clinical trial phases (Phase I, II, III, IV)
• overall_status: Current study status

Study Design & Population

• enrollment_count: Target or actual enrollment number
• minimum_age / maximum_age: Age eligibility bounds
• sex: Gender eligibility (All, Male, Female)
• healthy_volunteers: Whether healthy volunteers accepted
• eligibility_criteria: Detailed inclusion/exclusion criteria
• study_population: Target population description

Clinical Context

• conditions: Medical conditions studied
• keywords: Study-related keywords
• brief_summary: Study purpose and rationale
• detailed_description: Comprehensive study description
• primary_outcomes / secondary_outcomes: Measured endpoints

Organizational Information

• lead_sponsor: Primary study sponsor
• collaborators: Additional supporting organizations
• locations: Study sites with geographic coordinates
• overall_officials: Principal investigators and study officials

Temporal Data

• study_first_submit_date: Initial submission to ClinicalTrials.gov
• start_date: Study start date
• primary_completion_date: Primary endpoint completion
• completion_date: Overall study completion
• last_update_submit_date: Most recent data update

Enhanced Features

• brief_summary_embedding: 768-dim semantic embedding of study summary
• eligibility_criteria_embedding: 768-dim embedding of eligibility text

Usage Examples

Basic Data Loading

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("louisbrulenaudet/clinical-trials")

# Access train split
train_data = dataset["train"]

print(f"Dataset size: {len(train_data)}")
print(f"Features: {train_data.features}")

Advanced usage and embeddings

from datasets import load_dataset
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
  "thomas-sounack/BioClinical-ModernBERT-base", device="cuda", model_kwargs={"torch_dtype": "float16"}
)

dataset = load_dataset("louisbrulenaudet/clinical-trials", split="train")

columns_to_embed = [
  "brief_summary",
  "eligibility_criteria"
]

def embed_texts(batch):
  for col in columns_to_embed:
    embeddings = model.encode(batch[col], convert_to_numpy=True, normalize_embeddings=True)
    batch[f"{col}_embedding"] = embeddings.tolist()
  return batch

embedded_dataset = hf_dataset.map(
  embed_texts,
  batched=True,
  batch_size=512,
  desc="Embedding columns"
)

Applications

This dataset enables various research and development applications:

Clinical Research

• Study Landscape Analysis: Understanding research trends and gaps
• Protocol Optimization: Learning from successful study designs
• Site Selection: Geographic analysis for multi-center trials
• Regulatory Intelligence: Phase progression and approval patterns

Machine Learning

• Text Classification: Categorizing studies by therapeutic area
• Information Extraction: Parsing eligibility criteria and outcomes
• Similarity Search: Finding related studies using embeddings
• Trend Prediction: Forecasting research directions

Healthcare Analytics

• Disease Burden Analysis: Understanding research investment by condition
• Innovation Tracking: Monitoring emerging therapies and interventions
• Collaboration Networks: Analyzing sponsor and investigator relationships
• Geographic Health Mapping: Regional research activity patterns

Data Limitations & Considerations

Coverage Limitations

• Source Dependency: Limited to studies registered on ClinicalTrials.gov
• Registration Bias: Not all studies worldwide are required to register
• Temporal Scope: Historical data quality varies by submission period
• Language: Primarily English-language studies

Data Quality Notes

• Self-Reported: Information accuracy depends on sponsor reporting
• Update Lag: Some studies may have outdated status information
• Completeness: Optional fields may be sparse for older studies
• Standardization: Free-text fields may lack consistent formatting

Ethical Considerations

• Privacy: No individual participant data included
• Transparency: Enhances clinical research transparency
• Research Bias: May reflect existing healthcare disparities
• Access: Promotes equitable access to clinical research information

Licensing & Attribution

Dataset License

This dataset is released under the MIT License, allowing for both academic and commercial use with proper attribution.

Source Attribution

• Primary Source: ClinicalTrials.gov (https://clinicaltrials.gov/)
• API: ClinicalTrials.gov API v2

Citation

If you use this dataset in your research, please cite:

@dataset{louisbrulenaudet2025,
  title={Clinical Trials Dataset: Comprehensive ClinicalTrials.gov Data for Semantic analysis},
  author={[Louis Brulé Naudet]},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/louisbrulenaudet/clinical-trials}
}

This dataset aims to accelerate biomedical research by providing easy access to comprehensive clinical trial information. We encourage responsible use that advances medical knowledge and improves patient outcomes.

Feedback

If you have any feedback, please reach out at louisbrulenaudet@icloud.com.