Datasets:

richardyoung
/

synthea-575k-patients

license: mit
task_categories:
  - text-generation
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - healthcare
  - synthetic-data
  - medical
  - clinical
  - ehr
  - electronic-health-records
  - synthea
  - education
  - tutorial
  - beginner-friendly
size_categories:
  - 100K<n<1M

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

Table	Description	Key Fields
patients	Patient demographics	birthdate, gender, race, ethnicity, city, state
conditions	Diagnoses/conditions	code, description, start/stop dates
medications	Prescriptions	code, description, start/stop, reason
encounters	Clinical visits	type, code, description, cost
procedures	Medical procedures	code, description, cost
observations	Lab results & vitals	code, description, value, units
allergies	Patient allergies	code, description, type
immunizations	Vaccination records	code, description, date
careplans	Treatment plans	code, description, reason

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:

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)