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Datasets:
xpertsystems
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hc01-t2d-sample

Tasks:
Tabular Classification
Tabular Regression
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
Tags:
healthcare
diabetes
synthetic-data
ehr
clinical
type-2-diabetes
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metadata 
license: cc-by-nc-4.0
language:
  - en
tags:
  - healthcare
  - diabetes
  - synthetic-data
  - ehr
  - clinical
  - type-2-diabetes
  - tabular
  - longitudinal
pretty_name: HC01 — Synthetic Type 2 Diabetes Dataset (Sample)
size_categories:
  - 10K<n<100K
task_categories:
  - tabular-classification
  - tabular-regression
  - time-series-forecasting
configs:
  - config_name: patient_master
    data_files: data/patient_master.csv
  - config_name: encounters
    data_files: data/patient_encounters.csv
  - config_name: medications
    data_files: data/medication_orders.csv
  - config_name: complications
    data_files: data/complications_registry.csv
  - config_name: labs
    data_files: data/lab_results_longitudinal.csv
  - config_name: population_summary
    data_files: data/population_summary.csv

HC01 — Synthetic Type 2 Diabetes Patient Dataset (Evaluation Sample)

Publisher: XpertSystems.ai SKU: HC01 (sample) Version: 1.0.0 License: CC BY-NC 4.0 — non-commercial evaluation and research use only. Commercial use, redistribution, or derivative data products require a commercial license. Full product: Contact pradeep@xpertsystems.ai

What this is

A 500-patient evaluation slice of the XpertSystems HC01 synthetic Type 2 Diabetes dataset, released for technical evaluation, academic research, and benchmarking. The full commercial product covers 25,000+ patients with complete statistical validation, ML feature packs, and a Grade A+ benchmark report.

This sample is intended to let ML engineers, data scientists, and health-economics researchers verify the statistical fidelity and schema quality of the data before evaluating the full product. It is not sized for model training at production scale — rare events, long tails, and cross-cohort signal are materially underrepresented at 500 patients.

What's included

Six CSV files covering a 5-year patient journey:

File Rows (approx.) Description
patient_master.csv 500 One row per patient. Demographics, SDOH risk, diagnosis date, baseline biomarkers, comorbidities, insurance, care-site assignment.
patient_encounters.csv ~8,400 Longitudinal encounters (office, telehealth, specialist, ED, inpatient, RPM, care management). Includes biomarkers at visit, ICD-10, provider, payer, copay, care-gap flags.
medication_orders.csv ~6,200 Prescription orders across 10 T2D drug classes (metformin, SGLT2, GLP-1, insulin, etc.). Includes MPR/PDC adherence, prior authorization outcomes, formulary tier, titration and discontinuation events.
complications_registry.csv ~960 Diabetic complications (nephropathy, retinopathy, neuropathy, CVD, amputation, etc.) with onset date, severity stage, referral and treatment flags.
lab_results_longitudinal.csv ~19,600 HbA1c, fasting glucose, lipid panel, UACR, eGFR, and screening labs. Includes critical-value flags, follow-up lag, duplicate-lab and care-gap anomaly flags.
population_summary.csv ~600 Care-site × quarter aggregates: panel size, utilization rates, population-level glycemic control, care-gap rates.

Not included in this sample: the simulation engine, ML feature pack, statistical validation report (metrics.json), benchmark scoring artifacts, and the full-volume dataset.

Quick start 

from datasets import load_dataset

# Load any of the six tables
patients = load_dataset("xpertsystems/hc01-t2d-sample", "patient_master")
encounters = load_dataset("xpertsystems/hc01-t2d-sample", "encounters")
labs = load_dataset("xpertsystems/hc01-t2d-sample", "labs")

print(patients["train"][0])

Or with pandas directly:

import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="xpertsystems/hc01-t2d-sample",
    filename="data/patient_master.csv",
    repo_type="dataset",
)
df = pd.read_csv(path)

Schema highlights

Entity keys: patient_id (PAT#######) links all tables. encounter_id, order_id, lab_id, complication_id are unique per-row. site_id and payer_id link encounters to care sites and payers respectively.

Temporal structure: A 5-year simulated observation window. Quarterly patient-state updates drive encounter, lab, and medication timing. Dates are ISO-format (YYYY-MM-DD).

Coding standards: ICD-10-CM for diagnoses and complications; RxNorm-style codes for medications (representative, not authoritative); LOINC-aligned lab types.

Realism controls present in this sample:

  • Anomaly flags on labs, encounters, and medication orders for data-quality testing
  • Duplicate-lab and care-gap anomalies at calibrated base rates
  • Prior-authorization denial cascades affecting adherence
  • Coverage disruption events with downstream adherence penalties
  • Death flags with dates (where applicable)
  • SDOH-driven adherence heterogeneity

How this was generated

HC01 is produced by a deterministic simulation engine that models a synthetic T2D patient population through a calibrated sequence of stochastic processes. Patient demographics, comorbidities, and social-determinant risk are sampled from distributions aligned to public U.S. population references (CDC National Diabetes Statistics Report, NHANES, HEDIS MY2023). Each patient's HbA1c trajectory is modeled as a mean-reverting stochastic process conditioned on adherence, treatment intensification, and seasonal variation. Medication adherence (MPR/PDC) is drawn from a Beta distribution and modified by prior-authorization outcomes, copay burden, and coverage disruptions. Complication incidence follows a proportional-hazards formulation with hazard ratios for HbA1c, disease duration, CKD stage, and blood pressure, calibrated to published rates. Encounter, lab, and medication-order streams are generated conditional on patient state at each quarter, with ordering rates aligned to HEDIS Comprehensive Diabetes Care benchmarks. A small, controlled fraction of anomalies (duplicate labs, implausible values, care gaps) is injected to support data-quality and anomaly-detection use cases.

All simulation is deterministic under a fixed integer seed. The full commercial product ships with a 12-metric benchmark validation report certifying fidelity to published clinical and utilization targets (Grade A+ at default parameters).

Methodology references

  • American Diabetes Association — Standards of Care 2024
  • CDC — National Diabetes Statistics Report 2022
  • UKPDS Outcomes Model 2 (Hayes et al., 2013)
  • NCQA HEDIS MY2023 — Comprehensive Diabetes Care
  • HCUP — National ED Survey / National Inpatient Sample
  • AHIP — Prior Authorization Survey 2023
  • Nathan et al. (2008) — Estimated Average Glucose equation
  • CAP Q-Probes — Critical lab notification study

Suggested evaluation workflow

  1. Schema & volume sanity check. Load all six CSVs, confirm row counts and join integrity on patient_id.
  2. Distribution checks. Verify baseline HbA1c mean (8.2%), BMI distribution (33.2 kg/m² mean), comorbidity prevalences, and insurance mix against the references above.
  3. Correlation checks. HbA1c–BMI correlation (~0.28), HbA1c–complication incidence monotonicity, adherence–outcome relationships.
  4. Longitudinal behavior. Plot individual HbA1c trajectories; verify mean reversion, seasonal component, and separation between adherent and non-adherent cohorts.
  5. Edge-case coverage. Review anomaly flags, critical-lab follow-up patterns, prior-auth denial cascades.

If the sample passes your evaluation, the full 25,000-patient product (plus ML feature pack and Grade A+ validation report) is available under commercial license.

Citation

If you use this sample in research or publication, please cite:

XpertSystems.ai (2026). HC01 — Synthetic Type 2 Diabetes Patient Dataset (Evaluation Sample), v1.0.0. https://xpertsystems.ai

Contact

License

This sample is released under Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0). You may use, share, and adapt the data for non-commercial research and evaluation purposes with attribution. Commercial use, redistribution as a data product, or inclusion in a commercial offering requires a separate commercial license from XpertSystems.ai.

All records are fully synthetic. No real patient data, PHI, or PII is present. Not intended for clinical use.