---
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](https://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

```python
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

```python
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

```python
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

```python
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](https://huggingface.co/datasets/xpertsystems/hlt001-sample) — Synthetic Patient Population (5K patients × 79 cols, CDC/NHANES calibrated)
- [HLT-002](https://huggingface.co/datasets/xpertsystems/hlt002-sample) — Synthetic EHR Dataset (4K encounters + FHIR R4 bundles)
- [HLT-003](https://huggingface.co/datasets/xpertsystems/hlt003-sample) — 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:

```bibtex
@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

- **Web:** [https://xpertsystems.ai](https://xpertsystems.ai)
- **Email:** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai)
- **Full product catalog:** Cybersecurity, Insurance & Risk, Materials & Energy, Oil & Gas, Healthcare, and more

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