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hlt004-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Languages:
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Size:
10K<n<100K
Tags:
Synthetic
healthcare
disease-progression
longitudinal
survival-analysis
oncology
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metadata 
license: cc-by-nc-4.0
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
language:
  - en
tags:
  - synthetic
  - healthcare
  - disease-progression
  - longitudinal
  - survival-analysis
  - oncology
  - cardiology
  - nsclc
  - lung-cancer
  - heart-failure
  - seer
  - tnm-staging
  - nyha
  - recist
  - ctcae
  - biomarkers
  - kaplan-meier
  - competing-risk
  - fine-gray
  - markov-chain
  - gaussian-process
  - treatment-response
  - progression-free-survival
  - overall-survival
pretty_name: >-
  HLT-004 Synthetic Disease Progression Dataset — NSCLC + Heart Failure (Sample
  Preview)
size_categories:
  - 10K<n<100K

HLT-004 — Synthetic Disease Progression Dataset (Sample Preview)

A free, schema-identical preview of the full HLT-004 commercial product from XpertSystems.ai.

A fully synthetic longitudinal disease progression dataset combining patient-level baseline records, visit-level biomarker trajectories, and structured event logs across two clinically distinct disease modules: NSCLC (oncology survival) and Heart Failure (chronic cardiovascular progression). Calibrated to SEER 5-year survival, AJCC TNM 8th Edition staging, NYHA Functional Classification, RECIST 1.1 response criteria, CTCAE v5.0 adverse events, and Fine-Gray competing-risk survival outcomes.

⚠️ PRIVACY & SYNTHETIC NATURE Every record in this dataset is 100% synthetic. No real patient data, no PHI, no re-identifiable records. Stage-specific survival distributions and treatment response rates match published SEER / clinical trial benchmarks but the patients are computationally generated.

What's in this sample

Two disease modules, each with three CSVs:

nsclc/ — Non-Small Cell Lung Cancer (TNM 8th Edition staging)

File Rows Cols Description
hlt004_baseline.csv 400 37 Patient-level: demographics, ECOG PS, CCI, stage at dx, OS/PFS, death cause, 1L treatment arm, best overall response (CR/PR/SD/PD)
hlt004_longitudinal.csv 3,323 33 Visit-level: ~8.3 visits/patient × 5yr follow-up; CEA biomarker trajectory, labs, AEs, on-treatment flag, RECIST per imaging visit
hlt004_events.csv 1,943 4 Event log: diagnosis / treatment_start_1L / treatment_end_1L / response_assessment / progression / os_endpoint

heart_failure/ — Heart Failure (NYHA Class I-IV)

File Rows Cols Description
hlt004_baseline.csv 400 37 Patient-level: demographics, ECOG-equivalent ambulation, CCI, NYHA Class at dx, OS/PFS-equivalent, death cause, 1L therapy arm (GDMT/Ivabradine/LVAD)
hlt004_longitudinal.csv 4,502 33 Visit-level: ~11.3 visits/patient over 5yr; NT-proBNP biomarker, labs, AEs
hlt004_events.csv 1,969 4 Event log analogous to NSCLC structure

Total: ~12,500 rows across 8 CSVs + 2 generator summaries = ~2.2 MB.

Schema highlights

Baseline (37 columns, patient-level)

Identity: patient_id, disease_type, diagnosis_date

Demographics: age_at_dx, sex, race_ethnicity, bmi_baseline, smoking_ever, pack_years, insurance_type

Clinical scoring: ecog_ps_baseline (0-4), cci_at_dx (Charlson Comorbidity Index), stage_at_dx, stage_idx_at_dx (numeric)

Survival (Fine-Gray competing risk): os_days, os_event (death/censored), pfs_days, pfs_event, censoring_reason, death_cause, last_contact_days

Treatment: treatment_arm_1L (regimen code), best_overall_response (CR/PR/SD/PD per RECIST 1.1), response_day_from_dx, progression_day_from_dx

Longitudinal (33 columns, visit-level)

Visit metadata: patient_id, visit_id, visit_number, visit_date, days_from_dx, visit_type (baseline/imaging/routine_lab/urgent/hospitalization)

Disease state: stage_current, stage_idx_current, stage_changed_flag (Markov transitions)

Biomarker (disease-specific): NSCLC → cea_value (CEA, ng/mL); HF → ntprobnp_value (NT-proBNP, pg/mL); plus *_units

Treatment state: on_treatment_flag, treatment_line, treatment_arm

RECIST 1.1: recist_response_visit (CR/PR/SD/PD/N/A per imaging visit, derived from marker change)

Labs (10 panels): wbc, hgb, plt, anc, creatinine, bun, alt, ast, albumin, bilirubin

Adverse events (CTCAE v5.0): ae_count_grade1, ae_count_grade2, ae_count_grade3, ae_count_grade4

Events (4 columns, sparse event log)

patient_id, event_type ∈ {diagnosis, treatment_start_1L, treatment_end_1L, response_assessment, progression, os_endpoint}, event_day (days from dx), event_detail

Coverage

2 fully-calibrated disease modules demonstrated:

  • NSCLC (oncology) — TNM 8th Edition, SEER-calibrated OS by stage, CEA biomarker, 4 treatment regimens (Carboplatin/Pemetrexed/Pembrolizumab, Carboplatin/Paclitaxel, Pembrolizumab mono, Docetaxel 2L)
  • Heart Failure (chronic CV) — NYHA Class I-IV, 3 treatment lines (GDMT, Ivabradine add-on, LVAD)

Survival modeling:

  • Weibull-distributed time-to-event with shape parameter 1.2
  • Stage-specific median OS calibrated to SEER (NSCLC) and Pocock et al. 2013 (HF)
  • Competing-risk death cause assignment (Fine-Gray framework)
  • Administrative censoring at follow-up end

Biomarker dynamics:

  • Gaussian-process trajectories with stage-mean drift
  • Response-driven dips (CR/PR) and progression-driven rises
  • Disease-specific markers calibrated to clinical literature

Stage transitions:

  • Markov-chain time-inhomogeneous transitions
  • Stage 1→2→3→4 progression probabilities tied to PFS days

Adverse events:

  • CTCAE v5.0 grade 1-4 counts per visit
  • On-treatment elevation, ECOG-driven baseline rates

Missing data:

  • MAR rate 10% + MCAR rate 2% applied to biomarker/lab columns

Calibration source story

The full HLT-004 generator anchors all distributions to authoritative oncology and cardiology references:

  • SEER (Surveillance, Epidemiology, End Results) Program — 5-year overall survival benchmarks by cancer stage (NSCLC Stage I=63%, II=37%, III=15%, IV=6%)
  • AJCC TNM 8th Edition (2017) — Cancer staging anatomical definitions
  • NYHA Functional Classification (1994 update) — Heart Failure stages I-IV with associated 1-year mortality 5%/15%/30%/50%
  • Roche et al. (2020) Lancet — Advanced NSCLC median OS 10-14 months with platinum chemotherapy
  • Pocock et al. (2013) — MAGGIC heart failure risk model; chronic HF mortality predictors
  • RECIST 1.1 (Eisenhauer et al. 2009) — Response Evaluation Criteria In Solid Tumors
  • CTCAE v5.0 (NCI) — Common Terminology Criteria for Adverse Events
  • Fine & Gray (1999) — Competing risk regression for cause-specific mortality
  • CONSORT 2010 — Longitudinal data hygiene conventions

Sample-scale validation scorecard

Metric Observed Target Tolerance Status Source
NSCLC overall mortality rate 76.5% 77% ±10% ✅ PASS Roche et al. (2020)
NSCLC median OS (months) 13.8 12.0 ±4.0 ✅ PASS SEER 2021
HF overall mortality rate 62.5% 63% ±10% ✅ PASS Pocock et al. (2013)
HF median OS (months) 19.5 18.0 ±6.0 ✅ PASS Pocock et al. (2013)
RECIST response categories 4 4 — ✅ PASS RECIST 1.1
TNM stage diversity 4 4 — ✅ PASS AJCC TNM 8th
Longitudinal temporal monotonicity 100% 100% ±2% ✅ PASS CONSORT
PFS ≤ OS invariant 100% 100% ±2% ✅ PASS Fine & Gray (1999)
Event log completeness 100% 100% ±5% ✅ PASS Data hygiene
Missing data rate 11.8% 12% ±6% ✅ PASS MAR 10% + MCAR 2%

Grade: A+ (100/100) — verified across 6 random seeds (42, 7, 123, 2024, 99, 1).

Loading examples

Pandas 

import pandas as pd

base = pd.read_csv("nsclc/hlt004_baseline.csv")
long = pd.read_csv("nsclc/hlt004_longitudinal.csv")
events = pd.read_csv("nsclc/hlt004_events.csv")

print(f"Patients: {len(base)}")
print(f"Visits: {len(long)}")
print(f"Events: {len(events)}")

# Survival by stage
print("\nNSCLC OS by stage (event rate, median OS days):")
for stage, grp in base.groupby("stage_at_dx"):
    events_only = grp[grp["os_event"] == 1]
    if len(events_only):
        print(f"  {stage}: n={len(grp)}  "
              f"event_rate={grp['os_event'].mean():.2%}  "
              f"median_os={events_only['os_days'].median():.0f}d")

Hugging Face Datasets 

from datasets import load_dataset

ds = load_dataset("xpertsystems/hlt004-sample", data_files={
    "nsclc_baseline":      "nsclc/hlt004_baseline.csv",
    "nsclc_longitudinal":  "nsclc/hlt004_longitudinal.csv",
    "nsclc_events":        "nsclc/hlt004_events.csv",
    "hf_baseline":         "heart_failure/hlt004_baseline.csv",
    "hf_longitudinal":     "heart_failure/hlt004_longitudinal.csv",
    "hf_events":           "heart_failure/hlt004_events.csv",
})
print(ds)

Kaplan-Meier survival curve 

import pandas as pd
import matplotlib.pyplot as plt
# Optional: pip install lifelines
from lifelines import KaplanMeierFitter

base = pd.read_csv("nsclc/hlt004_baseline.csv")
fig, ax = plt.subplots(figsize=(8, 5))
for stage, grp in base.groupby("stage_at_dx"):
    kmf = KaplanMeierFitter()
    kmf.fit(grp["os_days"], event_observed=grp["os_event"], label=stage)
    kmf.plot_survival_function(ax=ax)
ax.set_xlabel("Days from diagnosis"); ax.set_ylabel("Overall Survival")
ax.set_title("NSCLC OS by TNM Stage")
plt.show()

Time-varying covariate Cox model 

import pandas as pd
from lifelines import CoxTimeVaryingFitter

long = pd.read_csv("nsclc/hlt004_longitudinal.csv")
base = pd.read_csv("nsclc/hlt004_baseline.csv")[["patient_id", "os_days", "os_event"]]

# Build start/stop format for time-varying biomarker
long_sorted = long.sort_values(["patient_id", "days_from_dx"])
long_sorted["start"] = long_sorted.groupby("patient_id")["days_from_dx"].shift(0)
long_sorted["stop"] = long_sorted.groupby("patient_id")["days_from_dx"].shift(-1)
long_sorted = long_sorted.merge(base, on="patient_id")
long_sorted["event_at_stop"] = (long_sorted["stop"] >= long_sorted["os_days"]) & (long_sorted["os_event"] == 1)
long_sorted = long_sorted.dropna(subset=["stop", "cea_value"])

ctv = CoxTimeVaryingFitter()
ctv.fit(long_sorted[["patient_id", "start", "stop", "event_at_stop", "cea_value"]]
        .rename(columns={"event_at_stop": "event"}),
        id_col="patient_id", event_col="event", start_col="start", stop_col="stop")
ctv.print_summary()

Suggested use cases

  • Survival modeling — Cox proportional hazards, Kaplan-Meier, accelerated failure time, parametric survival (Weibull, log-normal, log-logistic)
  • Time-varying covariate analysis — joint longitudinal-survival models using biomarker trajectories
  • Competing-risk regression — Fine-Gray subdistribution hazard models on death_cause field
  • Stage transition modeling — multi-state Markov models on stage_current evolution
  • Biomarker trajectory clustering — latent class growth analysis on CEA / NT-proBNP series
  • Treatment effect estimation — propensity score / inverse probability weighting from observational treatment_arm_1L assignment
  • RECIST response prediction — predict CR/PR/SD/PD from baseline + early biomarker dynamics
  • Adverse event hazard modeling — predict Grade 3+ AE onset from on-treatment patient state
  • Missing data methodology — develop MAR/MCAR/MNAR imputation strategies on flagged data
  • ML model pretraining — pretrain healthcare survival models before fine-tuning on real registry data (SEER, CIBMTR, etc.)

Sample vs. full product

Aspect This sample Full HLT-004 product
Disease modules 2 (NSCLC + HF) 4 fully-calibrated + 11 templated (15 total)
Patients per disease 400 10,000+ (default) up to 100K
Follow-up window 5 years Configurable 1-15 years
Schema identical identical
Calibration identical identical
License CC-BY-NC-4.0 Commercial license

The full product includes:

  • All 4 fully-calibrated diseases: NSCLC, Heart Failure, CKD, and Breast Cancer (after stage-distribution fix)
  • 11 additional disease modules (Colorectal, Prostate, Ovarian, COPD, T2DM Progressive, Multiple Sclerosis, Alzheimer's, Hepatocellular Carcinoma, AML, Rheumatoid Arthritis, HIV/AIDS) — these currently use NSCLC defaults as templates pending per-disease calibration
  • Up to 100K patients per disease for production-grade model training
  • Configurable follow-up windows up to 15 years

Contact us for the full product and calibration roadmap.

Limitations & honest disclosures

  • Sample is preview-only. 400 patients per disease × 5yr follow-up is enough to demonstrate schema, calibration, and survival shape, but is not statistically sufficient for serious prognostic model training, especially for rare death-cause analysis or biomarker discovery. Use the full product (10K+ patients per disease) for serious work.
  • Two disease modules in this sample, not all 15. The full HLT-004 catalogue lists 15 disease keys, but currently only 4 (NSCLC, Heart Failure, CKD, Breast Cancer) have per-disease calibration; the remaining 11 use NSCLC defaults as placeholder templates. This sample includes the 2 most-requested modules (NSCLC for oncology, Heart Failure for chronic CV).
  • breast_cancer module has a known stage-distribution normalization bug in the current generator release (stage_dist_dx sums to 0.94 instead of 1.0, causing a NumPy ValueError when sampling). This is a single one-line fix in the generator's catalogue but is not yet patched in the version distributed with this sample. We excluded breast_cancer from this preview rather than patch around it. Will be addressed in the next generator release.
  • Visit-level recist_response_visit is change-detection, not best-response. RECIST 1.1 best response per patient is in baseline.csv → best_overall_response. The visit-level field compares consecutive biomarker readings to flag changes during follow-up; it rarely yields "CR" since CR requires lesion disappearance (not capturable from a single marker comparison). Use best_overall_response for cohort-level response analysis.
  • Biomarker trajectories are simulation, not real labs. Stage-specific drift and response/progression-driven trajectories follow published clinical patterns (CEA elevation with NSCLC progression, NT-proBNP elevation with HF decompensation) but do NOT capture the full noise structure, batch effects, or measurement variability of real lab data. Use for ML algorithm development; validate on real registry data.
  • Treatment assignment is propensity-weighted, not randomized. Treatment arms are assigned based on stage, ECOG PS, CCI, and insurance — a non-randomized mechanism reflecting real-world prescribing. Causal effect estimation requires propensity score adjustment, IPW, or G-methods.
  • Stage transitions are Markov, not informative. Stage changes follow a memoryless transition kernel; the full real-world progression dynamics include informative censoring patterns not captured here.
  • Death causes are simplified. death_cause field includes the primary cause; real death certificates have ICD-10 underlying + contributing causes which are not in this sample.
  • CCI (Charlson) score is baseline only. Real comorbidity index evolves over follow-up; the sample treats it as fixed at diagnosis.

Ethical use guidance

This dataset is designed for:

  • Survival analysis methodology development
  • Healthcare ML model pretraining
  • Educational use in oncology biostatistics and HF outcomes research
  • Synthetic data validation methodology research
  • ETL pipeline testing for clinical registries (SEER-conformant schemas)

This dataset is not appropriate for:

  • Making decisions about real individual patients
  • Clinical prognosis claims of any kind
  • Drug efficacy or treatment comparisons in regulatory submissions
  • Training models that produce real clinical recommendations
  • Discriminatory analyses on protected demographic groups

Companion datasets in the Healthcare vertical

  • HLT-001 — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
  • HLT-002 — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
  • HLT-003 — Synthetic Clinical Trial Dataset (3 endpoint types + power sweep)
  • HLT-004 — Synthetic Disease Progression Dataset (you are here)

Use HLT-001 → HLT-004 together for population → encounter → trial → longitudinal-progression healthcare ML workflows.

Citation

If you use this dataset, please cite:

@dataset{xpertsystems_hlt004_sample_2026,
  author       = {XpertSystems.ai},
  title        = {HLT-004 Synthetic Disease Progression Dataset (Sample Preview)},
  year         = 2026,
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/xpertsystems/hlt004-sample}
}

Contact

Sample License: CC-BY-NC-4.0 (Creative Commons Attribution-NonCommercial 4.0) Full product License: Commercial — please contact for pricing.