TinyEHR : A Tiny Electronic Health Records Dataset for Learning, Prototyping, and Building

TinyEHR is a small, open, reproducible clinical dataset with 100 patients available in two formats - MIMIC and OMOP. It is derived from the MIMIC-IV Clinical Database Demo v2.2, the publicly available subset of MIMIC-IV published by the MIT Laboratory for Computational Physiology.

Openly available, no credentialing or data use agreements required. Install and start exploring clinical data in seconds.

Install

pip install tinyehr

Python API

import tinyehr

# Quick reference of all functions
tinyehr.help()

# Overview of all tables with row counts
tinyehr.info()
tinyehr.info(format="tinyehr_omop_format")

# List table names
tinyehr.list_tables()
tinyehr.list_tables(format="tinyehr_omop_format")

# Column names, types, and sample rows for a table
tinyehr.describe_table("patients")
tinyehr.describe_table("person", format="tinyehr_omop_format")

# Find tables by keyword in table and column names
tinyehr.search_tables("lab")
tinyehr.search_tables("drug")

# Load a table as a pandas DataFrame
patients = tinyehr.load_table("patients")
person = tinyehr.load_table("person", format="tinyehr_omop_format")

# All data for one patient across all tables
data = tinyehr.get_patient(10000032)
data["admissions"]    # DataFrame of this patient's admissions
data["labevents"]     # DataFrame of this patient's labs
data["noteevents"]    # DataFrame of this patient's notes

# Build a local SQLite database
db_path = tinyehr.build_sqlite(format="tinyehr_mimic_format")
db_path = tinyehr.build_sqlite(format="tinyehr_omop_format")

# Query the SQLite database
import sqlite3
conn = sqlite3.connect(db_path)
conn.execute("SELECT * FROM admissions LIMIT 5").fetchall()

Direct from HuggingFace

import pandas as pd

patients = pd.read_parquet(
    "hf://datasets/vidulpanickan/tinyehr/tinyehr_mimic_format/patients.parquet"
)

No dependencies beyond pandas and pyarrow.

Trouble downloading?

You can download the raw CSV files directly from GitHub:

1. Go to github.com/vidulpanickan/TinyEHR
2. Click the green Code button
3. Select Download ZIP

Or clone via terminal:

git clone https://github.com/vidulpanickan/TinyEHR.git

Formats

TinyEHR ships in two formats from the same underlying patient cohort:

MIMIC format follows the original MIMIC-IV schema with dates shifted to realistic years, ICD codes reformatted with decimal points, and 4,580 clinical notes generated using LLM based on patient visit profiles.

OMOP format follows the OHDSI CDM v5.3.1 schema with hashed person IDs, dates shifted to realistic years, and clinical codes mapped to standardized medical vocabularies. ICD codes in source_value fields are stored without decimal points, following the OMOP billing/claims convention.

For full dataset structure, schema documentation, and table details, visit About The Data.

Differences from MIMIC-IV Demo

TinyEHR applies four targeted transformations to the original MIMIC-IV Demo data. All clinical values, patient demographics, table structures, referential integrity, and row counts are unchanged.

What's New (v0.2.0)

• Parquet type enforcement: all column types now match the official MIMIC-IV and OMOP CDM DDL schemas exactly (integers, floats, timestamps, strings)
• CSV type enforcement: when loading CSVs without pyarrow, types are applied from bundled DDL schema files instead of relying on pandas auto-inference
• OMOP source values: no longer formatted with decimal points, preserving the billing/claims convention
• ICD-9 procedure codes: decimal point now correctly placed after 2nd digit (3961 → 39.61)
• Clinical notes: regenerated from patient profiles with correct admission dates

License

• Code (this Python package): MIT License
• Data (the TinyEHR dataset): ODbL-1.0