Synthetic Metabolic Datasets
This page demonstrates the structure of synthetic datasets available in this category, including variable schema, preview data, and reproducibility using the Syntherx SDK.
Example Dataset
Metabolic Outcomes Trial
Variable schema and example preview from the blueprint definition.
Variable Schema
| Column Name | Type | Description |
|---|---|---|
| patient_id | string | Unique synthetic patient identifier |
| age | number | Synthetic patient age |
| sex | string | Synthetic patient sex |
| treatment_group | string | Treatment or control group |
| baseline_measure | number | Baseline metabolic marker (e.g. composite index) |
| outcome_measure | number | Outcome metabolic marker at follow-up |
Data Preview
First 9 rows (preview only)
| patient_id | age | sex | treatment_group | baseline_measure | outcome_measure |
|---|---|---|---|---|---|
| P000001 | 71 | Female | treatment | 8.3 | 6.8 |
| P000002 | 43 | Male | control | 9 | 7.1 |
| P000003 | 59 | Female | treatment | 8.8 | 7.5 |
| P000004 | 67 | Male | control | 9.1 | 7.5 |
| P000005 | 44 | Male | control | 8.9 | 7 |
| P000006 | 65 | Male | treatment | 7.9 | 6.9 |
| P000007 | 51 | Female | treatment | 9.6 | 8.3 |
| P000008 | 53 | Male | control | 8.7 | 8 |
| P000009 | 51 | Female | treatment | 8.6 | 7.4 |
Includes CSV, JSON, and Parquet — ready for ML pipelines
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="metabolic_outcomes_trial",
rows=5000
)
df.to_csv("metabolic_outcomes_trial.csv")Use Cases
- Metabolic syndrome and biomarker modeling
- Intervention response and longitudinal outcomes
- Risk prediction for cardiometabolic endpoints
Privacy-Safe Synthetic Dataset
- Contains no real patient data
- Generated using statistical simulation
- Designed for machine learning research
Purchase Dataset
Research Dataset — $99
Secure checkout via Stripe.
Includes CSV, JSON, and Parquet — ready for ML pipelines
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