Hospital Readmission

Name: Hospital Readmission
Creator: Syntherx

Synthetic dataset for 30-day hospital readmission prediction. Includes admission characteristics, discharge disposition, and readmission outcomes.

This resource represents a fully synthetic cohort patterned after hospital scenarios: there are no real patients or protected health information, only statistically plausible records for method development and reproducible benchmarks.

Rows include variables such as patient_id, age, sex, admission_diagnosis, discharge_disposition, readmission_30d. You can inspect the full schema and representative preview below before downloading or generating a fresh cohort with the Syntherx SDK.

Teams use datasets like this for AI and statistical modeling, digital twin and pathway simulation, curriculum and sandbox environments, and cross-institutional collaborations where sharing real data is impractical.

Research Dataset — $99

Secure checkout via Stripe.

Includes CSV, JSON, and Parquet — ready for ML pipelines

Variable Schema

Column Name	Type	Description
patient_id	string	Unique synthetic patient identifier
age	number	Synthetic patient age
sex	string	Synthetic patient sex
admission_diagnosis	string	Primary admission diagnosis
discharge_disposition	string	Discharge disposition
readmission_30d	number	1 if readmitted within 30 days

Data Preview

First 9 rows (preview only)

Includes CSV, JSON, and Parquet — ready for ML pipelines

patient_id	age	sex	admission_diagnosis	discharge_disposition	readmission_30d
P000001	72	Female	Heart Failure	Home	1
P000002	65	Male	Pneumonia	Rehab Facility	0
P000003	58	Female	Diabetes Complications	Home	0
P000004	77	Male	Stroke	Skilled Nursing Facility	1
P000005	69	Female	COPD	Home Health Care	0
P000006	61	Male	Kidney Failure	Dialysis Center	1
P000007	55	Female	Hypertension	Home	0
P000008	73	Male	Sepsis	ICU Step-down	1
P000009	64	Female	Arrhythmia	Home	0

Reproduce This Dataset

Recreate this dataset in Python (Jupyter, Kaggle, or Google Colab) using the Syntherx SDK.

# Install Syntherx SDK
pip install syntherx

from syntherx import generate_dataset

df = generate_dataset(
    blueprint="hospital_readmission",
    rows=5000
)

df.to_csv("hospital_readmission.csv")

Use Cases

Build and validate AI/ML pipelines for Hospital scenarios without using real patient data.
Train and evaluate models on structured fields such as patient_id, age, sex, admission_diagnosis.
Run simulations, power analyses, and exploratory analytics in a privacy-safe sandbox.
Prototype dashboards, ETL flows, and feature stores before touching production systems.

Dataset Characteristics

Fully synthetic — no PHI; suitable for sharing, teaching, and external collaboration.
Schema includes 6 variables: patient_id, age, sex, admission_diagnosis, discharge_disposition, readmission_30d
Delivered in researcher-friendly formats (CSV, JSON, Parquet) for downstream tooling.
Generated with the Syntherx simulation engine for reproducible cohort-scale draws.

Privacy-Safe Synthetic Dataset

Contains no real patient data
Generated using statistical simulation
Designed for machine learning research

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