| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - time-series-forecasting |
| tags: |
| - synthetic-data |
| - healthcare |
| - cardiology |
| - hypertension |
| - htn |
| - high-blood-pressure |
| - blood-pressure-monitoring |
| - abpm |
| - ambulatory-bp-monitoring |
| - home-bp-monitoring |
| - central-aortic-pressure |
| - pulse-wave-velocity |
| - pwv |
| - augmentation-index |
| - arterial-stiffness |
| - bp-variability |
| - white-coat-hypertension |
| - masked-hypertension |
| - resistant-hypertension |
| - nocturnal-dipping |
| - non-dipper |
| - reverse-dipper |
| - ace-inhibitor |
| - arb |
| - calcium-channel-blocker |
| - ccb |
| - thiazide |
| - beta-blocker |
| - mra |
| - spironolactone |
| - antihypertensive |
| - medication-adherence |
| - bp-response |
| - side-effects |
| - pill-burden |
| - lifestyle-modification |
| - dash-diet |
| - sodium-intake |
| - physical-activity |
| - mets |
| - sleep-quality |
| - osa |
| - obstructive-sleep-apnea |
| - ascvd |
| - ascvd-pooled-cohort |
| - framingham-risk |
| - pooled-cohort-equation |
| - mace |
| - major-adverse-cardiovascular-event |
| - mi-prediction |
| - stroke-prediction |
| - hf-hospitalization |
| - atrial-fibrillation |
| - cv-death |
| - lvh |
| - left-ventricular-hypertrophy |
| - lv-mass-index |
| - e-e-prime |
| - diastolic-dysfunction |
| - carotid-imt |
| - carotid-plaque |
| - retinopathy |
| - microalbuminuria |
| - macroalbuminuria |
| - uacr |
| - ckd |
| - kdigo |
| - egfr |
| - ckd-epi |
| - ckd-stage |
| - hs-crp |
| - bnp |
| - troponin |
| - acc-aha-2017 |
| - esh-2018 |
| - abpm-task-force |
| - carey-resistant-htn |
| - aha-acc-pce-2013 |
| - longitudinal-ehr |
| - ehr-synthetic |
| - clinical-trial-simulation |
| pretty_name: HCCAR003 — Synthetic Hypertension & Cardiovascular Risk Dataset (Sample) |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: hccar003_dataset.parquet |
| --- |
| |
| # HCCAR003 — Synthetic Hypertension & Cardiovascular Risk Dataset (Sample Preview) |
|
|
| **XpertSystems.ai | Synthetic Data Factory | Healthcare / Cardiology Vertical** |
|
|
| A **longitudinal hypertension cohort dataset** with quarterly visit-level |
| records spanning **7 clinical modules**: BP monitoring (office, home, ABPM |
| 24hr/day/night with dipping pattern, central aortic, pulse wave velocity, |
| augmentation index, BP variability), antihypertensive medications (up to |
| 4 drug slots, 8 drug classes — ACEi, ARB, CCB, Thiazide, Beta-blocker, |
| Alpha-blocker, MRA), lifestyle factors (DASH diet, dietary sodium, |
| physical activity METs, sleep, smoking, alcohol, stress), biomarkers |
| (full lipid panel, hs-CRP, creatinine, eGFR with KDIGO CKD stage, UACR |
| with microalbuminuria/macroalbuminuria, electrolytes, glucose, HbA1c, |
| BNP, troponin, ASCVD 10y risk, Framingham), end-organ damage (LVH, LV |
| mass index, LVEF, E/e' ratio, carotid IMT, plaque, retinopathy grade, |
| white matter lesion volume, lacunar infarcts), and **MACE outcomes** |
| (MI, ischemic / hemorrhagic stroke, TIA, HF hospitalization, AF new |
| onset, CV death, all-cause death, study dropout). |
|
|
| Calibrated benchmark-first against **ACC/AHA 2017 Hypertension Guidelines** |
| (Whelton et al.), **ACC/AHA 2013 Pooled Cohort Equations** (Goff et al.), |
| **KDIGO 2012 CKD Classification**, **ABPM Task Force / ESH 2013 |
| Recommendations**, **AHA/ACC Resistant Hypertension Scientific Statement |
| (Carey et al. 2018)**, and **ESH/ESC 2018 Hypertension Guidelines**. |
|
|
| This is the **sample preview** — 150 patients × ~12 quarterly visits over |
| 3 years (~1,800 visit records, ~1.1 MB). The full product covers 10,000+ |
| patients × full 10-year follow-up (40 quarterly visits) with extended |
| medication titration histories, multi-cuff measurement protocols, and |
| pre-built scenario configs for SPRINT-style intensive vs standard BP |
| target trials, salt-sensitive hypertension studies, and resistant |
| hypertension subgroup analysis. |
|
|
| --- |
|
|
| ## Dataset summary |
|
|
| | Table | Rows (sample) | What it contains | |
| |---|---:|---| |
| | `hccar003_dataset` | 1,747 | One row per patient × visit. 99 features across 7 clinical modules (demographics carried forward + BP monitoring + medications + lifestyle + biomarkers + end-organ damage + MACE outcomes). 150 unique patients with up to 12 quarterly visits each (some patients drop out due to death or withdrawal) | |
|
|
| Provided in both **CSV** and **Parquet**. Aggregate to patient-level via |
| `groupby('patient_id')` for cross-sectional analysis. |
|
|
| --- |
|
|
| ## Calibration sources |
|
|
| All ten validation metrics target named clinical / regulatory standards: |
|
|
| - **ACC/AHA 2017 Hypertension Guidelines** (Whelton et al. 2018) — HTN |
| stage classification (Normal, Elevated, Stage 1, Stage 2, Crisis) |
| - **ACC/AHA 2013 Pooled Cohort Equations** (Goff et al. 2014) — 10-year |
| ASCVD risk (race/sex-specific, baseline column included with known |
| calibration issues — see Limitations) |
| - **KDIGO 2012 CKD Classification** — eGFR-based G1-G5 staging, |
| UACR-based A1-A3 albuminuria categories |
| - **ABPM Task Force / ESH 2013** (O'Brien et al. 2013) — ambulatory |
| blood pressure monitoring definitions, dipping pattern classification, |
| masked hypertension criteria |
| - **AHA/ACC Resistant Hypertension Scientific Statement** (Carey et al. |
| 2018) — resistant HTN = SBP ≥130/80 on ≥3 antihypertensives |
| - **ESH/ESC 2018 Hypertension Guidelines** (Williams et al. 2018) — |
| European HTN management framework |
| - **CKD-EPI 2009** (Levey et al. 2009) — eGFR calculation (note: the |
| generator uses the OLD race-coefficient version; the 2021 NKF-ASN |
| refit removed the race coefficient. See Limitations.) |
| - **Survival analysis monotonicity** — CV death ⊆ all-cause death |
|
|
| --- |
|
|
| ## Validation scorecard (seed = 42) |
|
|
| 10/10 PASS · **Grade A+ (100%)** across all six canonical seeds (42, 7, 123, 2024, 99, 1). |
|
|
| | # | Metric | Observed | Target | Tol | Type | Source | |
| |---|---|---:|---:|---:|---|---| |
| | 1 | `pulse_pressure_equals_sbp_minus_dbp_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity | |
| | 2 | `map_equals_dbp_plus_pp_third_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity | |
| | 3 | `ckd_stage_matches_kdigo_egfr_rate` | 0.999 | 0.99 | ±0.01 | FLOOR | KDIGO 2012 | |
| | 4 | `albuminuria_flags_match_uacr_bands_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | KDIGO ACR thresholds | |
| | 5 | `abpm_dipping_pattern_matches_dip_pct_rate` | 0.991 | 0.99 | ±0.01 | FLOOR | ABPM Task Force / ESH 2013 | |
| | 6 | `resistant_htn_requires_3_drugs_and_sbp_130_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Carey et al. 2018 | |
| | 7 | `masked_htn_definition_match_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ABPM Task Force | |
| | 8 | `cv_death_implies_all_cause_death_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Survival monotonicity | |
| | 9 | `mace_event_flag_matches_event_type_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | MACE composite endpoint | |
| | 10 | `bp_in_physiologic_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Physiologic plausibility | |
|
|
| --- |
|
|
| ## Schema highlights (99 cols) |
|
|
| ### Identity & demographics (8 cols, carried per visit) |
| `patient_id` (HC-CAR-XXXXXX), `visit_number` (1-12), `visit_date`, |
| `years_from_baseline`, `age_at_visit`, `sex` (Male / Female), |
| `race_ethnicity` (NonHispanic_White / NonHispanic_Black / Hispanic / |
| Asian / Other), `htn_stage_baseline` (Normal / Elevated / Stage1_HTN / |
| Stage2_HTN / Crisis). |
|
|
| ### BP monitoring (19 cols) |
| `sbp_office_mmhg`, `dbp_office_mmhg`, `pp_office_mmhg`, `map_office_mmhg`, |
| `sbp_home_avg_mmhg`, `dbp_home_avg_mmhg`, `abpm_sbp_24hr_mmhg`, |
| `abpm_dbp_24hr_mmhg`, `abpm_sbp_daytime_mmhg`, `abpm_sbp_nighttime_mmhg`, |
| `abpm_dipping_pct`, `abpm_dipping_pattern` (Reverse_dipper / Non-dipper / |
| Dipper / Extreme_dipper), `central_aortic_sbp_mmhg`, |
| `pulse_wave_velocity_ms`, `augmentation_index_pct`, `bp_variability_sd_sbp`, |
| `white_coat_effect_mmhg`, `white_coat_flag`, `masked_hypertension_flag`. |
|
|
| ### Medications (12 cols) |
| `drug_class_{1-4}`, `drug_name_{1-3}`, `drug_dose_1`, |
| `n_antihypertensive_drugs` (0-4), `medication_adherence_pct`, |
| `bp_response_sbp_mmhg`, `resistant_htn_flag`, `side_effect_code` |
| (Dry_Cough / Peripheral_Edema / Hypokalemia / Bradycardia / |
| Orthostatic_Hypotension / Hyperkalemia / None), `pill_burden_score`. |
| |
| ### Lifestyle (8 cols) |
| `bmi_kg_m2`, `dietary_sodium_mg_day`, `dash_diet_score`, |
| `physical_activity_mets_hr_wk`, `sleep_hours_night`, `smoking_status` |
| (Never / Former / Current), `alcohol_drinks_week`, `stress_score`. |
|
|
| ### Biomarkers (22 cols) |
| `total_cholesterol_mg_dl`, `ldl_cholesterol_mg_dl`, `hdl_cholesterol_mg_dl`, |
| `triglycerides_mg_dl`, `non_hdl_cholesterol_mg_dl`, `hs_crp_mg_l`, |
| `creatinine_mg_dl`, `egfr_ml_min_1_73m2`, `uacr_mg_g`, `ckd_stage` |
| (G1 / G2 / G3a / G3b / G4 / G5), `bun_mg_dl`, `potassium_meq_l`, |
| `sodium_meq_l`, `glucose_fasting_mg_dl`, `hba1c_pct`, `uric_acid_mg_dl`, |
| `bnp_pg_ml`, `troponin_i_ng_l`, `ascvd_10yr_risk_pct`, |
| `framingham_risk_score_pct`, `microalbuminuria_flag`, |
| `macroalbuminuria_flag`. |
|
|
| ### End-organ damage (9 cols) |
| `lvh_flag`, `lv_mass_index_g_m2`, `lvef_pct`, `e_e_prime_ratio`, |
| `carotid_imt_mm`, `carotid_plaque_flag`, `retinopathy_grade` (0-4), |
| `wml_volume_ml`, `lacunar_infarct_flag`. |
|
|
| ### MACE outcomes (11 cols) |
| `mace_event_flag`, `mace_event_type` (MI / Stroke_Ischemic / |
| Stroke_Hemorrhagic / TIA / HF_Hospitalization / AF_New_Onset / CV_Death / |
| Non_CV_Death / None), `mi_flag`, `stroke_flag`, `stroke_type`, |
| `hf_hospitalization_flag`, `af_new_onset_flag`, `cardiovascular_death_flag`, |
| `all_cause_death_flag`, `study_dropout_flag`, `dropout_reason` (Death / |
| Withdrawal / None). |
|
|
| ### Comorbidities (7 cols, carried forward) |
| `diabetes_flag`, `dyslipidemia_flag`, `osa_flag`, `statin_use_flag`, |
| `aspirin_use_flag`, `family_history_htn_flag`, |
| `charlson_comorbidity_index`. |
|
|
| --- |
|
|
| ## Suggested use cases |
|
|
| - **BP control prediction** — train classifiers/regressors on |
| `sbp_office_mmhg` and `resistant_htn_flag` from baseline + medication |
| + lifestyle features |
| - **ABPM interpretation ML** — predict dipping pattern, masked HTN from |
| office BP, home BP, and patient features; useful for ABPM-replacement |
| algorithms |
| - **Medication response prediction** — model `bp_response_sbp_mmhg` |
| given drug class combinations and patient characteristics (uplift |
| modeling for personalized antihypertensive selection) |
| - **Resistant HTN cohort identification** — classifier for |
| `resistant_htn_flag` for utilization analytics |
| - **Antihypertensive adherence ML** — predict |
| `medication_adherence_pct` from pill burden, side effects, SES, age |
| - **ASCVD risk recalibration** — train improved 10-year ASCVD models |
| to compare against ACC/AHA PCE (note: the included |
| `ascvd_10yr_risk_pct` has known calibration issues — useful for |
| derivation studies that explicitly correct PCE bugs) |
| - **MACE survival ML** — Cox / DeepSurv / random survival forests on |
| the MACE outcomes (aggregate per-visit flags to patient level for |
| TTE analysis) |
| - **End-organ damage progression** — model `lvh_flag`, `carotid_imt_mm`, |
| `wml_volume_ml` trajectories given longitudinal BP control |
| - **CKD progression in HTN** — model `egfr_ml_min_1_73m2` decline |
| trajectories; useful for predicting CKD-G3 → G4 transitions |
| - **White coat / masked HTN detection** — classifier for office vs |
| ambulatory discrepancy; useful for diagnostic workflow automation |
| - **Salt sensitivity studies** — use `dietary_sodium_mg_day` and |
| individual BP response to identify salt-sensitive phenotypes |
| - **Dipping pattern ML** — predict nocturnal dipping pattern |
| (Reverse / Non / Normal / Extreme) from office BP, age, OSA, |
| comorbidities; useful for ABPM-free phenotyping |
| - **Treatment intensification timing ML** — predict when next drug |
| should be added from BP trajectory + current regimen |
| - **Quality improvement / HEDIS analytics** — BP control rate |
| measurement, medication intensification audit (HEDIS Controlling |
| High Blood Pressure metric) |
|
|
| --- |
|
|
| ## Loading examples |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("xpertsystems/hccar003-sample", split="train") |
| print(ds.shape) |
| ``` |
|
|
| ```python |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar003-sample", "hccar003_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # HTN stage distribution |
| print(df.groupby("patient_id")["htn_stage_baseline"].first() |
| .value_counts(normalize=True).round(3)) |
| ``` |
|
|
| ```python |
| # BP trajectory by HTN stage |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar003-sample", "hccar003_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| trajectory = ( |
| df.groupby(["htn_stage_baseline", "visit_number"])["sbp_office_mmhg"] |
| .mean().unstack(level=0).round(1) |
| ) |
| print(trajectory.head(12)) |
| ``` |
|
|
| ```python |
| # Resistant HTN identification + drug regimens |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar003-sample", "hccar003_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Patients with resistant HTN at ANY visit |
| resistant_pts = df.loc[df["resistant_htn_flag"] == 1, "patient_id"].unique() |
| print(f"Resistant HTN patients: {len(resistant_pts)} / {df['patient_id'].nunique()}") |
| |
| # Their typical drug regimens (drug_class_1 distribution) |
| resist_df = df[df["patient_id"].isin(resistant_pts)] |
| print(resist_df["drug_class_1"].value_counts().head()) |
| ``` |
|
|
| ```python |
| # Aggregate MACE outcomes to patient level |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar003-sample", "hccar003_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| # Per-patient MACE summary (per-visit flags need aggregation) |
| patient_mace = df.groupby("patient_id").agg( |
| any_mi=("mi_flag", "max"), |
| any_stroke=("stroke_flag", "max"), |
| any_hf=("hf_hospitalization_flag", "max"), |
| any_af=("af_new_onset_flag", "max"), |
| cv_death=("cardiovascular_death_flag", "max"), |
| all_cause_death=("all_cause_death_flag", "max"), |
| follow_up_yrs=("years_from_baseline", "max"), |
| ).round(3) |
| |
| print("Patient-level event rates:") |
| print(patient_mace.mean().round(3)) |
| ``` |
|
|
| ```python |
| # ABPM dipping pattern distribution |
| import pandas as pd |
| from huggingface_hub import hf_hub_download |
| |
| df = pd.read_parquet(hf_hub_download( |
| "xpertsystems/hccar003-sample", "hccar003_dataset.parquet", |
| repo_type="dataset", |
| )) |
| |
| print("Dipping pattern distribution by HTN stage:") |
| print(pd.crosstab(df["htn_stage_baseline"], df["abpm_dipping_pattern"], |
| normalize="index").round(3)) |
| ``` |
|
|
| --- |
|
|
| ## Limitations and honest disclosures |
|
|
| This sample is calibrated for **structural fidelity, not bit-exact reproduction |
| of any specific HTN cohort registry.** Specifically: |
|
|
| - **The included Pooled Cohort Equation (`ascvd_10yr_risk_pct`) is BUGGY.** |
| The generator's PCE implementation (lines 122-153) has multiple errors: |
| (a) White Male branch uses `1.764` for both bp_treated and untreated, |
| removing treatment effect; (b) White Female has a misplaced |
| `-29.799*1` term; (c) Black Male is missing the published intercept; |
| (d) Black Female has implausible coefficients on ln_sbp (29.2907 vs |
| published 0.295). **Result: ASCVD risk values saturate at clip ceiling |
| (75%) for ~93% of patients and at clip floor (1%) for Black Males.** |
| Use the column only as a relative-ordering signal, not for absolute |
| 10-year ASCVD estimation. For accurate ASCVD risk, recompute from |
| age, sex, race, total cholesterol, HDL, SBP, BP treatment flag, |
| diabetes, smoking using the published formula |
| (Goff et al. 2014). |
| - **eGFR uses the OLD 2009 CKD-EPI formula with race coefficient** |
| (line 216-217: `egfr *= 1.159 if Black`). The 2021 NKF-ASN refit |
| REMOVED the race coefficient. If you need the modern formula, |
| recompute from `creatinine_mg_dl`, `age_at_visit`, `sex` without |
| the race multiplier. |
| - **`carotid_plaque_flag` formula bug**: `int(RNG.random() < (0.05 + 0.01*age - 0.5))` |
| effectively gives ZERO carotid plaque to patients under age 45 (the |
| probability term goes negative). For full plaque modeling, use the |
| full product or augment with separate carotid imaging modules. |
| - **MACE per-visit flags fire on only ONE visit per patient** (the |
| visit within 0.13 years of `event_time`). They are NOT cumulative — |
| the flag does not carry forward after the event. **For patient-level |
| MACE prediction, aggregate via `groupby('patient_id').max()` on the |
| flag columns** (see "Loading examples" above). Time-to-event must be |
| derived from the visit_number × 0.25 (quarterly) offset. |
| - **`af_new_onset_flag` can fire from MACE event AND from a separate |
| stochastic check** (line 480): `int(mace_type == 'AF_New_Onset' or |
| (yrs > 5 and sbp_off > 160 and random < 0.005))`. So AF can occur |
| outside of the formal MACE event window — by design, reflecting that |
| AF is sometimes diagnosed incidentally. |
| - **`_dropout_at` reference in main loop** (line 646) checks a dictionary |
| key that's never set by `generate_patient_baseline`. The branch is |
| dead code; dropout actually fires via `study_dropout_flag` only. |
| Cosmetic side-effect. |
| - **Mean SBP ~144 mmHg, mean DBP ~97 mmHg** in this sample — higher |
| than typical real-world HTN cohorts (~135/85) because the HTN stage |
| distribution skews toward Stage 1/2 (~55% of patients). The |
| generator's stage probabilities `[Normal: 15%, Elevated: 20%, |
| Stage1: 30%, Stage2: 25%, Crisis: 10%]` produce a hypertension- |
| enriched cohort by design (suitable for HTN clinical trials, not |
| general population epidemiology). |
| - **Race/ethnicity SBP offsets** (line 106-109): Black patients have |
| +6 mmHg SBP offset, Asian -2 mmHg. These match published trial |
| observations (e.g., AASK, ALLHAT) but are NOT a complete model of |
| hypertension disparities — they encode only the magnitude offset, not |
| the underlying mechanisms (RAAS responsiveness, salt sensitivity, |
| vascular dysfunction). |
| - **Visit dropout is independent of clinical state** (line 485: |
| `dropout_flag = int(dead_v or (random < 0.003))`). Real HTN cohort |
| dropout correlates with poor BP control, adverse drug effects, and |
| SES. Treat the sample as informatively-censored data only if you |
| augment with realistic dropout mechanisms. |
| - **Comorbidities are independent draws** (lines 188-194: dm, dys, ckd, |
| osa) — no realistic co-occurrence beyond per-flag base rates. Real |
| cardiometabolic clustering (diabetes + dyslipidemia + obesity + CKD) |
| is much tighter than the generator produces. |
| - **`scipy.stats` is imported but unused** in active generator code. |
| No external compute dependencies beyond numpy + pandas + tqdm. The |
| scipy distributions (norm, beta, lognorm, weibull_min) appear in the |
| import block but never get called. |
| - **Masked HTN observed at 0.3-0.5%** in sample — much lower than the |
| 10-15% prevalence reported in clinical literature. Generator's |
| `wc_effect ~ N(8, 6)` and white_coat→office subtraction produces |
| predominantly white-coat phenotype (office > home) rather than |
| masked (home > office). For masked HTN ML research, augment with |
| inverted white-coat scenarios from the full product. |
| - **Visit count varies by patient** — some patients have 12 visits, |
| some have fewer due to dropout. Use `groupby('patient_id').size()` |
| to check follow-up duration per patient. Treat as unbalanced panel |
| data. |
| - **Drug-drug interactions and titration are simplified.** The drug |
| regimen is fixed at baseline (4 slots, randomly chosen from 7 |
| classes); no realistic titration logic, no switching due to side |
| effects, no addition due to inadequate BP response. For |
| pharmacotherapy intensification ML, use the full product. |
|
|
| The full HCCAR003 product addresses these by corrected ACC/AHA PCE |
| implementation, full 2021 CKD-EPI refit, complete carotid plaque |
| modeling, MACE flag carry-forward for survival analysis, realistic |
| medication titration trajectories, dependent comorbidity sampling, |
| and pre-built scenario configs (SPRINT-style intensive vs standard, |
| salt-sensitive HTN, resistant HTN subgroup). Contact us for the |
| licensed commercial release. |
|
|
| --- |
|
|
| ## Companion datasets |
|
|
| This is the third SKU in our **Healthcare / Cardiology** vertical. Related |
| datasets from elsewhere in the catalog: |
|
|
| - [**HCCAR001**](https://huggingface.co/datasets/xpertsystems/hccar001-sample) |
| Heart Failure Dataset — chronic HF longitudinal records with GDMT, |
| device therapy, hospitalization, 12 quarterly visits |
| - [**HCCAR002**](https://huggingface.co/datasets/xpertsystems/hccar002-sample) |
| Acute Myocardial Infarction Dataset — STEMI/NSTEMI/UA with serial |
| troponin kinetics, intervention timing, in-hospital outcomes |
| - [**HCCAR003**](https://huggingface.co/datasets/xpertsystems/hccar003-sample) |
| Hypertension Dataset (you are here) — longitudinal HTN cohort with |
| ABPM, GDMT, MACE outcomes |
| - [**Healthcare / Neurology**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Insurance & Risk**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Energy & Climate**](https://huggingface.co/xpertsystems) (8 SKUs) |
| - [**Manufacturing**](https://huggingface.co/xpertsystems) (10 SKUs) |
| - [**Oil & Gas**](https://huggingface.co/xpertsystems) (17 SKUs) |
|
|
| **Cardiology pairing**: HCCAR001 + HCCAR002 + HCCAR003 covers the full |
| HTN→AMI→HF clinical trajectory. Hypertension is the leading modifiable |
| risk factor for AMI (HCCAR002) and HFpEF (HCCAR001 phenotype). |
|
|
| For the broader catalog, see https://huggingface.co/xpertsystems |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{xpertsystems_hccar003_sample_2026, |
| author = {XpertSystems.ai}, |
| title = {HCCAR003 Synthetic Hypertension \& Cardiovascular Risk Dataset (Sample Preview)}, |
| year = 2026, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/xpertsystems/hccar003-sample} |
| } |
| ``` |
|
|
| --- |
|
|
| ## Contact |
|
|
| - **Web:** https://xpertsystems.ai |
| - **Email:** pradeep@xpertsystems.ai |
| - **Full product catalog:** Cardiology, Neurology, Insurance & Risk, Energy |
| & Climate, Manufacturing, Oil & Gas, Cybersecurity, and more |
|
|
| **Sample License:** CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) |
| **Full product License:** Commercial — please contact for pricing. |
|
|
| **Important medical disclaimer:** This dataset contains SYNTHETIC patient |
| records only. No data was derived from any real patient, EHR archive, |
| or clinical registry. The dataset is intended for ML model development, |
| benchmarking, and education — NOT for clinical decision support, patient |
| counseling, or medical research conclusions. All clinical thresholds |
| (HTN stage, resistant HTN, ABPM dipping pattern, KDIGO CKD stages) are |
| sourced from published guidelines; users are responsible for verifying |
| against current ACC/AHA/ESC/KDIGO guidelines for clinical applications. |
| The included Pooled Cohort Equation implementation has known |
| calibration issues — see Limitations. |
|
|
