richardyoung/synthea-575k-patients

Synthea Synthetic Patient Records (575K Patients)

A comprehensive synthetic healthcare dataset containing 575,415 patients with complete medical histories, generated using Synthea — the gold standard for synthetic EHR data.

No real patient data. Fully synthetic, HIPAA-safe, and ready for ML research and education.

Why This Dataset?

• —575K patients with realistic demographics, conditions, medications, and encounters
• —Privacy-safe: No real PHI — use freely in research, teaching, and production
• —Complete records: Conditions, medications, procedures, encounters, observations, and more
• —Parquet format: Fast loading with pandas, polars, or HF datasets

Quick Start

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("richardyoung/synthea-575k-patients")

# Or load a specific table
patients = load_dataset("richardyoung/synthea-575k-patients", data_files="patients.parquet")

# Explore
print(f"Patients: {len(ds['train']):,}")
print(ds['train'].column_names)
print(ds['train'][0])

With pandas

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="richardyoung/synthea-575k-patients",
    filename="patients.parquet",
    repo_type="dataset",
)
df = pd.read_parquet(path)
print(df.head())
print(f"Shape: {df.shape}")

Dataset Structure

Use Cases

• —ML training: Build classifiers for disease prediction, readmission risk, mortality
• —NLP: Train models on clinical text and medical terminology
• —Education: Teach healthcare data science without privacy concerns
• —Benchmarking: Standardized dataset for comparing healthcare ML approaches
• —RAG systems: Build medical Q&A systems with realistic clinical data

Related Work

This dataset supports the CardioEmbed research project — domain-adapted embeddings for cardiology:

• —CardioEmbed (Qwen3-8B)
• —CardioEmbed-BioLinkBERT

Citation

If you use this dataset, please cite Synthea:

@article{walonoski2018synthea,
  title={Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record},
  author={Walonoski, Jason and others},
  journal={Journal of the American Medical Informatics Association},
  year={2018}
}

Other Models by richardyoung

• —Abliterated/Uncensored models: Qwen2.5-7B | Qwen3-14B | DeepSeek-R1-32B | Qwen3-8B
• —MLX quantizations (Apple Silicon): Kimi-K2 series | olmOCR MLX
• —OCR & Vision: olmOCR GGUF
• —Healthcare/Medical: Synthea 575K patients dataset | CardioEmbed
• —Research: LLM Instruction-Following Evaluation (arxiv:2510.18892)