This dataset contains 1 million synthetic patients with ancestry (European and South Asian), demographics, anthropometrics, labs, lifestyle/SES, comorbidities, medications, and time-to-event outcomes for ASCVD, heart failure, and atrial fibrillation. The dataset inspired by the published Circulation study.

All data are artificially generated and contain no identifiable patient records.

Dataset Structure

Demographics

id — unique patient identifier
ancestry — European or SouthAsian
age — years
sex_male — binary (1 = male, 0 = female)

Anthropometrics

height_cm, weight_kg, bmi
waist_cm, hip_cm, whr (waist ÷ hip)
waist_height_ratio, body_fat_pct, trunk_fat_pct

Blood Pressure

sbp_mmHg, dbp_mmHg
hypertension (binary)
on_bp_meds (binary)

Lipids

ldl_mgdl, hdl_mgdl, tg_mgdl
apoA_mgdl, apoB_mgdl, lpa_nmolL
on_statin (binary)

Metabolic / Inflammatory

glucose_mgdl, hba1c_pct, crp_mgL
diabetes (binary)
on_dm_meds (binary)

Renal

creatinine_mgdl, cystatin_c_mgL, egfr_ckd_epi
ckd (binary)

Lifestyle / SES

smoking_status (never / former / current)
pack_years
sedentary (binary)
pa_met_h_wk (physical activity in MET-hours/week)
diet_score
education_level
income_bracket
townsend (deprivation index)
psychosocial_stress
pct_life_in_uk

Family / History

family_history (binary)

Female-Specific (if sex_male = 0)

early_menopause, gest_htn, gest_dm, preeclampsia, iugr

Outcomes

time_ascvd_yrs, ascvd_event
time_hf_yrs, hf_event
time_af_yrs, af_event

(For outcomes: time_* = follow-up time in years (capped at 15), *_event = binary indicator)

Example Usage

import pandas as pd

df = pd.read_parquet("ukb_sa_ascvd_synthetic_1m.parquet")
print(df.shape)   # (1000000, ~70 columns)
print(df.head())

Intended Use

Educational & personal learning
Benchmarking methods for EMR preprocessing, feature extraction, and survival analysis
Synthetic data methodology testing

Not for clinical decision-making.

Downloads last month: 15