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+---
+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.

hccar003_dataset.parquet

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