Datasets:

Elmehdi67
/

Medical-Research

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

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

# 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.