Upload folder using huggingface_hub

README.md

@@ -0,0 +1,504 @@
+---
+license: cc-by-nc-4.0
+task_categories:
+- tabular-classification
+- tabular-regression
+- time-series-forecasting
+tags:
+- synthetic-data
+- healthcare
+- cardiology
+- heart-failure
+- hf
+- hfref
+- hfmref
+- hfpef
+- advanced-heart-failure
+- end-stage-heart-failure
+- nyha
+- lvef
+- ejection-fraction
+- echocardiography
+- echocardiogram
+- echo
+- diastolic-function
+- ase-2016
+- ea-ratio
+- e-e-prime
+- gls
+- global-longitudinal-strain
+- bnp
+- nt-probnp
+- troponin
+- biomarkers
+- ckd-epi
+- egfr
+- cardiorenal-syndrome
+- gdmt
+- guideline-directed-medical-therapy
+- 4-pillar
+- arni
+- sglt2i
+- entresto
+- jardiance
+- farxiga
+- beta-blocker
+- mra
+- spironolactone
+- ace-inhibitor
+- arb
+- icd
+- crt
+- lvad
+- heart-transplant
+- pinnacle-registry
+- aha-2022
+- acc-aha-guidelines
+- cms-hrrp
+- 30-day-readmission
+- readmission
+- mortality
+- mace
+- kccq
+- six-minute-walk
+- vo2-max
+- cpet
+- atrial-fibrillation
+- charlson-comorbidity
+- cardiometabolic
+- longitudinal-ehr
+- ehr-synthetic
+pretty_name: HCCAR001 — Synthetic Heart Failure Dataset (Sample)
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: baseline
+  data_files: hccar001_baseline.parquet
+- config_name: visits
+  data_files: hccar001_visits.parquet
+---
+
+# HCCAR001 — Synthetic Heart Failure Dataset (Sample Preview)
+
+**XpertSystems.ai | Synthetic Data Factory | Healthcare / Cardiology Vertical**
+
+A **two-table longitudinal heart failure patient dataset** spanning the
+full clinical-research data surface for HF cohorts: baseline patient
+records (~114 features per patient covering demographics, full
+echocardiographic assessment with diastolic function, 14 biomarkers
+including BNP/NT-proBNP/troponin/CKD-EPI eGFR, guideline-directed
+medical therapy (GDMT) 4-pillar prescribing, device therapy (ICD/CRT/
+LVAD/transplant), hospitalization outcomes, functional status (NYHA,
+6MWD, KCCQ, VO₂ max), comorbidities, and vital signs) plus quarterly
+follow-up visits over 3 years tracking LVEF/BNP/NYHA trajectories.
+
+Calibrated benchmark-first against **ACC/AHA 2022 Heart Failure
+Guidelines**, **ASE 2016 Recommendations for Evaluation of LV Diastolic
+Function**, **CKD-EPI 2009**, **PINNACLE Registry** (real-world GDMT
+prescribing rates), **CMS HRRP** (30-day readmission benchmarks),
+**SCD-HeFT / DINAMIT** (primary-prevention ICD criteria), and **CARE-HF
+/ COMPANION** (CRT eligibility criteria).
+
+This is the **sample preview** — 200 patients × 12 quarterly visits over
+3 years (200 baseline records + 2,400 visit records, ~500 KB). The full
+product covers 10,000+ patients × full 3-year follow-up with extended
+echocardiographic / CMR modules, full CPET / device interrogation
+detail, complete medication titration trajectories, and pre-built cohort
+configs for HFrEF clinical trial simulation, HFpEF treatment-effect
+heterogeneity studies, and cardiorenal-cardiometabolic comorbidity
+analysis.
+
+---
+
+## Dataset summary
+
+| Table | Rows (sample) | What it contains |
+|---|---:|---|
+| `baseline` | 200 | One row per patient. 114 features spanning: demographics + enrollment, cardiac function (LVEF, LV dimensions, GLS, RV function, TAPSE), diastolic function (E/A, E/e', LAVI, TRPG, diastolic grade), biomarkers (BNP, NT-proBNP, troponin I/T, creatinine, eGFR, BUN, electrolytes, hemoglobin, CRP, albumin, uric acid, iron studies, CKD stage), GDMT 4-pillar + ARNI/ivabradine/hydralazine-nitrate + device therapy (ICD/CRT/LVAD/transplant), hospitalization (LOS, ICU, 30/90/180-day readmission, ED visits, hospitalization risk score), functional outcomes (6MWD, KCCQ, VO₂ max, EF response, NYHA improvement, MACE, mortality, time-to-event), comorbidities (AF, diabetes, HTN, CAD, valvular, sleep apnea, COPD, anemia, obesity, depression, CCI), and vital signs (HR, SBP/DBP, MAP, weight, SpO₂, JVP, edema grade, rales) |
+| `visits` | 2,400 | Quarterly visit-level records over 12 visits × 3 years. LVEF trajectory, BNP trajectory, NYHA class updates, weight changes — useful for longitudinal modeling, treatment-response trajectories, and time-to-event ML |
+
+Both tables provided in **CSV** and **Parquet**. Join on `patient_id`.
+
+---
+
+## Calibration sources
+
+All ten validation metrics target named clinical / regulatory standards:
+
+- **ACC/AHA 2022 HF Guidelines** — HF phenotype LVEF bands (HFrEF ≤40%,
+  HFmrEF 41-49%, HFpEF ≥50%, Advanced <30%)
+- **ASE 2016** Recommendations for Evaluation of LV Diastolic Function —
+  E/A ratio, E/e', LAVI, TRPG diagnostic criteria
+- **Lang et al. (ASE 2015)** — chamber quantification recommendations
+  for LVEDV/LVESV/SV identities
+- **CKD-EPI 2009** — eGFR formula physiologic bounds [8, 140]
+- **PINNACLE Registry** — real-world GDMT 4-pillar prescribing rates
+  for outpatient HFrEF cohorts
+- **CMS HRRP** (Hospital Readmissions Reduction Program) — 30-day HF
+  readmission national benchmark 22-25% + temporal monotonicity
+- **SCD-HeFT (Bardy et al. 2005) + DINAMIT (Hohnloser et al. 2004)** —
+  primary prevention ICD eligibility (LVEF ≤35%, NYHA II-III)
+- **CARE-HF (Cleland et al. 2005) + COMPANION (Bristow et al. 2004)** —
+  CRT eligibility (LVEF ≤35%, NYHA III-IV ambulatory, LBBB)
+
+---
+
+## 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 | `lvef_in_phenotype_band_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACC/AHA 2022 |
+| 2 | `lvesv_equals_lvedv_times_one_minus_ef_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Lang et al. ASE 2015 |
+| 3 | `stroke_volume_equals_lvedv_minus_lvesv_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ASE 2015 |
+| 4 | `cardiac_index_equals_co_over_bsa_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | Hemodynamic identity |
+| 5 | `egfr_in_physiologic_bounds_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CKD-EPI 2009 |
+| 6 | `gdmt_4_pillar_requires_lvef_under_40_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ACC/AHA 2022 + PINNACLE |
+| 7 | `icd_requires_lvef_under_35_and_nyha_ge_2_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | SCD-HeFT / DINAMIT |
+| 8 | `crt_requires_lvef_under_35_and_nyha_ge_3_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CARE-HF / COMPANION |
+| 9 | `readmission_temporal_ordering_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | CMS HRRP |
+| 10 | `ea_ratio_equals_e_over_a_rate` | 1.000 | 0.99 | ±0.01 | FLOOR | ASE 2016 |
+
+---
+
+## Schema highlights
+
+### `baseline` (200 rows × 114 cols)
+
+**Demographics (11 cols):** `patient_id` (UUID), `site_id`,
+`hf_phenotype` (HFrEF / HFmrEF / HFpEF / Advanced), `age_at_baseline`,
+`sex_male`, `race_ethnicity` (5 categories), `bmi_kg_m2`, `bsa_m2`,
+`nyha_class_baseline` (1-4), `index_hospitalization`, `enrollment_date`.
+
+**Cardiac function (13 cols):** `lvef_pct_baseline`, `lvedd_mm_baseline`,
+`lvesd_mm_baseline`, `lvedv_ml_baseline`, `lvesv_ml_baseline`,
+`stroke_volume_ml_baseline`, `heart_rate_baseline_bpm`,
+`cardiac_output_l_min_baseline`, `cardiac_index_l_min_m2_baseline`,
+`gls_pct_baseline`, `rv_function_baseline`, `tapse_mm_baseline`,
+`echo_quality_baseline`.
+
+**Diastolic function (9 cols):** `diastolic_grade_baseline` (Grade_1 /
+Grade_2 / Grade_3 / Indeterminate), `e_velocity_cm_s_baseline`,
+`a_velocity_cm_s_baseline`, `ea_ratio_baseline`, `e_prime_cm_s_baseline`,
+`e_e_prime_ratio_baseline`, `lavi_ml_m2_baseline`, `trpg_mmhg_baseline`,
+`ivrt_ms_baseline`.
+
+**Biomarkers (15 cols):** `bnp_pg_ml_baseline`, `nt_probnp_pg_ml_baseline`,
+`troponin_i_ng_ml_baseline`, `troponin_t_ng_ml_baseline`,
+`creatinine_mg_dl_baseline`, `egfr_ml_min_173m2_baseline`,
+`bun_mg_dl_baseline`, `sodium_meq_l_baseline`, `potassium_meq_l_baseline`,
+`hemoglobin_g_dl_baseline`, `iron_studies_baseline`, `crp_mg_l_baseline`,
+`albumin_g_dl_baseline`, `uric_acid_mg_dl_baseline`, `ckd_stage_baseline`.
+
+**Treatment & GDMT (14 cols):** `gdmt_acei_arb_arni`, `gdmt_betablocker`,
+`gdmt_mra`, `gdmt_sglt2i`, `gdmt_4_pillar_flag`, `arni_flag`,
+`ivabradine_flag`, `hydralazine_nitrate_flag`, `icd_flag`, `crt_flag`,
+`lvad_flag`, `transplant_flag`, `diuretic_loop_dose_mg`,
+`gdmt_dose_optimization_pct`.
+
+**Hospitalization (13 cols):** `hospitalization_count_3yr`,
+`index_los_days`, `index_icu_flag`, `readmission_30d_flag`,
+`readmission_90d_flag`, `readmission_180d_flag`, `readmission_cause`,
+`iv_diuretic_flag`, `diuretic_response_ml_mg`,
+`discharge_congestion_score`, `ed_visit_count_1yr`,
+`days_to_readmission`, `hospitalization_risk_score`.
+
+**Functional outcomes (15 cols):** `six_mwd_meters_baseline`,
+`kccq_overall_score_baseline`, `vo2_max_ml_kg_min_baseline`,
+`lvef_change_12m_pct`, `ef_response_flag`, `nyha_improvement_flag`,
+`six_mwd_trend_12m_meters`, `kccq_change_12m`, `mace_flag_3yr`,
+`mortality_flag_3yr`, `cv_death_flag_3yr`, `time_to_death_days`,
+`borg_dyspnea_scale_baseline`, `patient_global_impression_12m`,
+`cpet_reason`.
+
+**Comorbidities & vitals (25 cols):** `atrial_fibrillation_flag`,
+`af_type`, `diabetes_flag`, `diabetes_type`, `hypertension_flag`,
+`cad_flag`, `prior_mi_flag`, `valvular_disease`, `sleep_apnea_flag`,
+`copd_flag`, `anemia_flag`, `obesity_flag`, `depression_flag`,
+`charlson_comorbidity_index`, `heart_rate_bpm_baseline`,
+`systolic_bp_mmhg_baseline`, `diastolic_bp_mmhg_baseline`,
+`pulse_pressure_mmhg_baseline`, `map_mmhg_baseline`,
+`weight_kg_baseline`, `oxygen_saturation_pct_baseline`,
+`jvp_cmh2o_baseline`, `peripheral_edema_grade_baseline`,
+`rales_flag_baseline`.
+
+### `visits` (2,400 rows × 11 cols)
+`patient_id`, `visit_number` (1-12), `visit_date`,
+`months_from_baseline` (0-33), `hf_phenotype`, `nyha_class_visit`,
+`lvef_pct_visit`, `lvef_trend_visit`, `bnp_pg_ml_visit`,
+`bnp_trend_pct_visit`, `weight_change_kg_visit`.
+
+---
+
+## Suggested use cases
+
+- **HF phenotype classification ML** — train classifiers for
+  HFrEF / HFmrEF / HFpEF / Advanced from echo + biomarker features
+- **30-day readmission prediction** — classifier on
+  `readmission_30d_flag` from demographics, biomarkers, GDMT status,
+  hospitalization risk features (CMS HRRP-relevant)
+- **GDMT optimization ML** — predict which patients are eligible for
+  4-pillar therapy, which are likely to achieve target doses
+- **Mortality / MACE risk stratification** — Cox models or survival
+  forests on `mortality_flag_3yr` + `time_to_death_days` with right-
+  censoring
+- **EF response prediction** — regressor for `lvef_change_12m_pct` from
+  baseline echo + GDMT + biomarkers; identifies likely responders
+- **Diastolic function classification** — train multi-class on
+  `diastolic_grade_baseline` (Grade 1/2/3/Indeterminate) from E/A, E/e',
+  LAVI features per ASE 2016
+- **Cardiorenal risk modeling** — joint modeling of `egfr_ml_min_173m2`
+  trajectory and `bnp_pg_ml` trajectory across visits; useful for
+  cardiorenal syndrome detection
+- **NLP-augmented EHR research** — table provides structured ground
+  truth for NLP extraction from clinical notes; pair with synthetic
+  clinical text generators
+- **Clinical trial cohort simulation** — filter to specific eligibility
+  criteria (e.g., HFrEF + NYHA II-III + LVEF ≤35% + eGFR ≥30) and
+  benchmark expected event rates / power calculations
+- **Treatment effect heterogeneity** — train uplift / CATE models for
+  SGLT2i, ARNI, MRA effects across phenotype subgroups
+- **Longitudinal LVEF / BNP trajectory clustering** — unsupervised
+  clustering on visit-level trajectories to discover responder
+  phenotypes
+- **NYHA class transitions** — Markov / state-space models on
+  `nyha_class_visit` transitions
+- **Device therapy appropriateness ML** — train models that flag
+  potential under-utilization of ICD/CRT in eligible patients
+- **Hospitalization cost / utilization modeling** — combine
+  `index_los_days`, `index_icu_flag`, `ed_visit_count_1yr`,
+  `hospitalization_count_3yr` for utilization predictive analytics
+
+---
+
+## Loading examples
+
+```python
+from datasets import load_dataset
+
+baseline = load_dataset("xpertsystems/hccar001-sample", "baseline", split="train")
+visits = load_dataset("xpertsystems/hccar001-sample", "visits", split="train")
+print(baseline.shape, visits.shape)
+```
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+baseline = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
+    repo_type="dataset",
+))
+visits = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_visits.parquet",
+    repo_type="dataset",
+))
+
+# HF phenotype distribution (ACC/AHA 2022)
+print(baseline["hf_phenotype"].value_counts(normalize=True).round(3))
+```
+
+```python
+# GDMT 4-pillar prescribing in HFrEF
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+baseline = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
+    repo_type="dataset",
+))
+
+hfref = baseline[baseline["hf_phenotype"] == "HFrEF"]
+gdmt_rates = hfref[[
+    "gdmt_acei_arb_arni", "gdmt_betablocker",
+    "gdmt_mra", "gdmt_sglt2i", "gdmt_4_pillar_flag"
+]].mean().round(3) * 100
+print("HFrEF GDMT prescribing rates (%):")
+print(gdmt_rates)
+```
+
+```python
+# Longitudinal LVEF trajectory by GDMT status
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+baseline = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
+    repo_type="dataset",
+))
+visits = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_visits.parquet",
+    repo_type="dataset",
+))
+
+joined = visits.merge(
+    baseline[["patient_id", "gdmt_4_pillar_flag", "gdmt_sglt2i"]],
+    on="patient_id"
+)
+joined["responder_group"] = joined["gdmt_4_pillar_flag"] | joined["gdmt_sglt2i"]
+trajectory = (
+    joined.groupby(["responder_group", "visit_number"])
+    ["lvef_pct_visit"].mean().unstack(level=0).round(2)
+)
+print(trajectory)  # responder group LVEF trajectory recovers; control doesn't
+```
+
+```python
+# 30-day readmission risk by HF phenotype
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+baseline = pd.read_parquet(hf_hub_download(
+    "xpertsystems/hccar001-sample", "hccar001_baseline.parquet",
+    repo_type="dataset",
+))
+
+risk = baseline.groupby("hf_phenotype").agg(
+    n=("patient_id", "count"),
+    readmit_30d_pct=("readmission_30d_flag", lambda x: x.mean() * 100),
+    readmit_90d_pct=("readmission_90d_flag", lambda x: x.mean() * 100),
+    mean_nyha=("nyha_class_baseline", "mean"),
+    mean_bnp=("bnp_pg_ml_baseline", "mean"),
+).round(2)
+print(risk)
+```
+
+---
+
+## Limitations and honest disclosures
+
+This sample is calibrated for **structural fidelity, not bit-exact reproduction
+of any specific HF registry or institutional cohort.** Specifically:
+
+- **30-day readmission rate observed ~30-35%** vs CMS HRRP national
+  benchmark 22-25%. The generator's `p_30d = 0.23 × (1 + risk × 0.8)`
+  risk-multiplier amplifies above the base rate. Use the TEMPORAL
+  ORDERING (30d ≤ 90d ≤ 180d) as the structural validation, not the
+  absolute rate. **For absolute-rate calibration, scale to the full
+  product or re-fit the risk multiplier.**
+- **3-year mortality observed ~10%** vs real-world HFrEF literature
+  25-35% (PARADIGM-HF, EMPEROR-Reduced, DAPA-HF placebo arms). The
+  generator computes `mort_p = mace_p × 0.45` with `mace_p` clipped
+  to [0.03, 0.65], producing a mort_p ceiling of ~0.29. **The
+  generator under-predicts mortality for advanced HF.**
+- **`bsa_m2` has a dead-code bug in the generator** (line 120 computes
+  BSA via Mosteller formula, line 121 immediately overwrites with
+  `N(1.9, 0.25)`). The overwritten N(1.9, 0.25) is what's in the
+  output. Don't use `bsa` for BMI/height back-calculation.
+- **LBBB is not modeled** for CRT eligibility — CARE-HF / COMPANION
+  criteria include LVEF ≤35% + NYHA III-IV ambulatory + LBBB. Our
+  validation covers the first two (LVEF + NYHA); LBBB status is
+  absent. Add this if you need full CRT-D appropriateness classification.
+- **Visit-level LVEF can drift OUT of baseline phenotype band** over
+  12 quarterly visits as patients respond to GDMT — this is realistic
+  clinical behavior (recovered EF). Our validation checks BASELINE LVEF
+  in phenotype band, not visit-level. Don't filter visits by
+  `lvef_pct_visit` matching baseline phenotype band — you'll lose
+  GDMT responders.
+- **`site_id` is randomly chosen from 30 generated UUIDs** —
+  patients within a site share no clustering structure (no
+  hierarchical / random-effects), no provider effects, no
+  geographic correlations. Treat as a nominal site label, not a
+  hierarchical clustering variable.
+- **HFpEF treatment effect modeling is simplified.** GDMT
+  prescribing rates from PINNACLE are anchored to outpatient
+  HFrEF cohorts; HFpEF GDMT rates (`acei_arb_arni: 0.55,
+  betablocker: 0.60, mra: 0.25, sglt2i: 0.45`) are reasonable
+  but the EF response to SGLT2i in HFpEF is muted (EMPEROR-
+  Preserved-style effects, not EMPEROR-Reduced). Our generator
+  applies a uniform `+1.5 ± 1` EF delta for SGLT2i regardless
+  of phenotype.
+- **Race / ethnicity distribution** (`60% Non-Hispanic White, 20%
+  Black/AA, 12% Hispanic, 5% Asian/Pacific Islander, 3% Other`)
+  matches general US prevalence but Black/AA HF prevalence is
+  ~2x higher than this in real registries. Adjust sampling if
+  modeling race-specific GDMT response (e.g., V-HeFT for
+  hydralazine-nitrate).
+- **Hydralazine-nitrate flag fires at 20% of Black/AA patients**
+  (line 389); the generator has no separate eligibility model
+  (ISDN should be ACEi-intolerant or sub-target dose). Treat
+  as an inclusive eligibility signal.
+- **Comorbidities are sampled independently** (lines 621-639) —
+  no realistic co-occurrence structure beyond per-phenotype
+  base rates. Real diabetes + obesity + sleep apnea + CKD
+  exhibit strong co-occurrence (cardiometabolic phenotype);
+  this generator treats them as independent.
+- **`scipy.stats`, `faker`, `tqdm` are mentioned in the generator's
+  docstring** but not used in active code. No external dependencies
+  beyond NumPy + Pandas.
+- **NYHA class distribution by phenotype** matches the design dict
+  (HFrEF [0.10, 0.35, 0.40, 0.15]) within ~5% sampling noise at
+  n=200. For tight matching, use the full product (10K patients).
+- **Visit count is fixed at 12 quarterly visits** — no realistic
+  censoring (loss to follow-up, death before visit, etc). All
+  patients have all 12 visits regardless of `mortality_flag_3yr`
+  or `time_to_death_days`. For survival ML, use baseline TTE
+  variables and don't naively use visit count as risk signal.
+
+The full HCCAR001 product addresses these by calibrated mortality and
+readmission rates against real-world HFrEF/HFpEF registries, hierarchical
+site / provider structure, dependent comorbidity sampling, full LBBB +
+QRS duration + bundle-branch block modeling for CRT eligibility, race-
+specific GDMT response heterogeneity, realistic censoring of visit
+records by death/LTFU, and pre-built scenario configs (HFrEF clinical
+trial simulation, HFpEF treatment-effect heterogeneity, cardiometabolic
+phenotype). Contact us for the licensed commercial release.
+
+---
+
+## Companion datasets
+
+This is the first SKU in our **Healthcare / Cardiology** vertical. Related
+datasets from elsewhere in the catalog:
+
+- [**Healthcare / Neurology**](https://huggingface.co/xpertsystems) (10 SKUs)
+  — synthetic neurological patient datasets covering stroke, MS, epilepsy,
+  Parkinson's, ALS, traumatic brain injury, dementia spectrum
+- [**Insurance & Risk**](https://huggingface.co/xpertsystems) (10 SKUs) —
+  health insurance claims, prior authorization, risk adjustment, MLR
+  modeling
+- [**Energy & Climate**](https://huggingface.co/xpertsystems) (8 SKUs) —
+  power grid, renewables, demand, O&G, smart grid, energy trading, climate
+  impact, consumer electricity
+- [**Manufacturing**](https://huggingface.co/xpertsystems) (10 SKUs) —
+  reliability, quality, operations, supply chain, IIoT
+- [**Oil & Gas**](https://huggingface.co/xpertsystems) (17 SKUs) — upstream,
+  midstream, downstream, geological / seismic, reservoir simulation
+
+For the broader catalog, see https://huggingface.co/xpertsystems
+
+---
+
+## Citation
+
+```bibtex
+@dataset{xpertsystems_hccar001_sample_2026,
+  author       = {XpertSystems.ai},
+  title        = {HCCAR001 Synthetic Heart Failure Dataset (Sample Preview)},
+  year         = 2026,
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/datasets/xpertsystems/hccar001-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
+(GDMT eligibility, ICD/CRT criteria, biomarker ranges) are sourced from
+published guidelines; users are responsible for verifying against current
+ACC/AHA/HFSA guidelines for clinical applications.

hccar001_baseline.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ac8aa1d14a010718702c49e8901e7cd85b040e9d619ed922c045ff441ae4cbd
+size 132319

hccar001_visits.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfdc31afbca29237b7d0713e97d3fc4cc4278a19f72d4dba72ec6eb71c8761d0
+size 54848