| --- |
| annotations_creators: |
| - expert-generated |
| language: |
| - en |
| license: |
| - cc0-1.0 |
| multicity: |
| - false |
| size_categories: |
| - 100K<n<1M |
| source_datasets: |
| - original |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| task_ids: |
| - synthetic-data |
| pretty_name: "SynData medical research Dataset (500000 rows)" |
| dataset_info: |
| features: |
| - name: patient_guid |
| dtype: string |
| description: "Patient GUID" |
| - name: age_years |
| dtype: float64 |
| description: "Age (Years)" |
| - name: gender_cat |
| dtype: string |
| description: "Gender Category" |
| - name: bmi_index |
| dtype: float64 |
| description: "BMI Index" |
| - name: systolic_bp_mmHg |
| dtype: float64 |
| description: "Systolic BP (mmHg)" |
| - name: crp_biomarker_mg_L |
| dtype: float64 |
| description: "C-Reactive Protein (mg/L)" |
| - name: hba1c_pct |
| dtype: float64 |
| description: "HbA1c (%)" |
| - name: treatment_arm |
| dtype: string |
| description: "Treatment Arm" |
| - name: survival_months |
| dtype: float64 |
| description: "Follow-up Survival (Mo)" |
| - name: outcome_flag |
| dtype: bool |
| description: "Primary Endpoint Met" |
| - name: time_series_vitals |
| dtype: string |
| description: "Vitals Telemetry Series" |
| splits: |
| - name: train |
| num_bytes: 130799834 |
| num_examples: 500000 |
| tags: |
| - synthetic |
| - differential-privacy |
| - monte-carlo |
| - gemini-ai |
| - tabular |
| - huggingface |
| --- |
| |
| # Dataset Card for SynData medical research Synthetic Dataset |
|
|
| ## Dataset Description |
|
|
| - **Homepage:** https://huggingface.co/datasets/my-org-or-user/syndata-medical-research |
| - **Repository:** my-org-or-user/syndata-medical-research |
| - **Point of Contact:** SynData Studio (my-org-or-user) |
| - **Dataset Size:** 500000 Rows / Columns: 11 |
| - **Generation Method:** Multivariate Monte Carlo Gaussian Copula |
| - **Differential Privacy Noise (Epsilon):** ε = 0.1 |
| - **PII Leakage Status:** 0% (Clean Anonymized Synthetic Generation) |
|
|
| ### Dataset Summary |
|
|
| This dataset is a fully synthetic, high-fidelity benchmark generated by **SynData Studio**. It maintains realistic mathematical covariance, non-linear feature correlations, and multi-modal time-series telemetry while guaranteeing zero exposure of real-world Personally Identifiable Information (PII) or Protected Health Information (PHI). |
|
|
| Designed for zero-shot ML training, synthetic pre-training, algorithm benchmarking, and cross-border privacy-preserving analytics. |
|
|
| ## Dataset Structure |
|
|
| ### Data Fields |
|
|
| - **`patient_guid`** (`uuid`): Patient GUID - De-identified HIPAA patient identifier |
| - **`age_years`** (`number`): Age (Years) - Participant age (18-88) |
| - **`gender_cat`** (`enum`): Gender Category - Biological sex assigned at birth |
| - **`bmi_index`** (`number`): BMI Index - Body Mass Index |
| - **`systolic_bp_mmHg`** (`number`): Systolic BP (mmHg) - Blood pressure reading |
| - **`crp_biomarker_mg_L`** (`number`): C-Reactive Protein (mg/L) - Inflammation marker |
| - **`hba1c_pct`** (`number`): HbA1c (%) - Glycated hemoglobin |
| - **`treatment_arm`** (`enum`): Treatment Arm - Trial cohort assignment |
| - **`survival_months`** (`number`): Follow-up Survival (Mo) - Observed trial period |
| - **`outcome_flag`** (`boolean`): Primary Endpoint Met - Boolean treatment response indicator |
| - **`time_series_vitals`** (`timestamp_series`): Vitals Telemetry Series - Multi-modal time-stamped ECG & SpO2 readings |
| |
| ### Sample Python Code to Load |
| |
| ```python |
| from datasets import load_dataset |
| import pandas as pd |
| |
| # Load synthetic dataset directly from Hugging Face Hub |
| ds = load_dataset("my-org-or-user/syndata-medical-research", split="train") |
| |
| # Convert to Pandas DataFrame |
| df = ds.to_pandas() |
| print(df.head()) |
| ``` |
| |
| ## Direct Python Upload Script |
| |
| ```python |
| # Install required libraries |
| # pip install huggingface_hub datasets pandas |
| |
| import pandas as pd |
| from datasets import Dataset |
| |
| # Load local synthetic dataset |
| df = pd.read_json("syndata_medical_research_500000_records.json") |
| |
| # Convert to Hugging Face Dataset object |
| hf_dataset = Dataset.from_pandas(df) |
| |
| # Push to Hugging Face Hub |
| hf_dataset.push_to_hub("my-org-or-user/syndata-medical-research", private=False) |
| ``` |
| |
| ## Licensing Information |
| |
| Dedicated to the public domain under **CC0 1.0 Universal (CC0 1.0) Public Domain Dedication**. Free for commercial and non-commercial model training without restriction. |
| |
