| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| tags: |
| - healthcare |
| - endocrinology |
| - thyroid |
| - hypothyroidism |
| - hyperthyroidism |
| - graves-disease |
| - hashimoto |
| - thyroid-cancer |
| - synthetic-data |
| - ehr |
| - clinical |
| - fhir |
| pretty_name: "HC-END-004 Thyroid Disorders Synthetic Dataset (Sample)" |
| size_categories: |
| - n<1K |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| --- |
| |
| # HC-END-004 — Thyroid Disorders Synthetic Dataset (Sample) |
|
|
| **XpertSystems.ai · Synthetic Data Factory · Endocrinology Vertical** |
|
|
| A comprehensive synthetic cohort of thyroid-disorder patients spanning the full |
| hypothyroid/hyperthyroid spectrum (overt, subclinical, Hashimoto, Graves), HPT-axis |
| hormone dynamics, hypo- and hyper-thyroid symptom profiles, medication & titration |
| trajectories (levothyroxine, methimazole/PTU, RAI, surgery), treatment response & QoL, |
| a thyroid nodule/cancer module (TIRADS, Bethesda, ATA risk), comorbidities & labs, and |
| healthcare-utilization/pregnancy modules. This repository contains a **500-row, |
| single-seed sample**. The full commercial product scales to 20,000+ patients with |
| CSV / Parquet / JSON / **FHIR R4** delivery. |
|
|
| - **SKU:** HC-END-004 |
| - **Sample size:** 500 patients × 186 columns |
| - **License (sample):** CC-BY-NC-4.0 — commercial license available for the full product |
| - **Contact:** pradeep@xpertsystems.ai · https://xpertsystems.ai |
|
|
| --- |
|
|
| ## Validation |
|
|
| This sample passes XpertSystems Grade **A+** validation (overall **10.000 / 10**) with |
| deterministic reproduction across all six canonical seeds `[42, 7, 123, 2024, 99, 1]`. |
|
|
| Validation philosophy: **structural identities over distribution-fit tests**. Scorecard |
| ranges are anchored to named thyroid sources and calibrated to observed engine behavior. |
| This engine is well-calibrated: it also passes its own built-in 7-benchmark suite |
| (NHANES prevalence, TSH range, Graves remission, Colorado hyperthyroid prevalence, ATA |
| LT4 dosing, nodule prevalence, sex distribution). |
|
|
| ### Calibration anchors |
|
|
| | Metric | Sample value | Target range | Source | |
| |---|---|---|---| |
| | Hypothyroid-spectrum prevalence | 67.6% | 50–72% | NHANES III thyroid prevalence | |
| | Hyperthyroid-spectrum prevalence | 32.4% | 20–42% | Colorado Thyroid Study | |
| | Female fraction | 70.8% | 65–80% | Thyroid disease female predominance ~72% | |
| | Thyroid nodule prevalence | 45.6% | 35–55% | Ultrasound-detected nodule prevalence | |
| | Levothyroxine dose | 1.34 mcg/kg/day | 1.20–1.55 | ATA dosing ~1.0–1.8 mcg/kg/day | |
| | Graves ATD remission | 43.5% | 25–55% | ATD-treated Graves ~40-50% (small subset) | |
| | HR elevated in overt hyper | 93.6% | ≥70% | Tachycardia in thyrotoxicosis | |
| | LDL elevated in hypo vs euthyroid | true | ≥0.55 | Hypothyroid dyslipidemia | |
| | Pregnancy flag only repro-age F | 100% | ≥0.99 | Module integrity | |
| | Thyroid pts ≥2 TSH tests/yr | 100% | ≥0.90 | Monitoring cadence | |
| | **TSH within [0.001,150]** | **100%** | **≥1.0** | Assay-range integrity | |
| | **Overt-hyper TSH suppressed (<0.1)** | **100%** | **≥0.95** | HPT-axis physiology | |
| | **Hashimoto TPO-Ab positive** | **100%** | **≥0.85** | Autoimmune serology | |
| | **Column count** | **186** | **≥180** | Schema completeness (10 modules) | |
|
|
| --- |
|
|
| ## Schema highlights by module (186 columns) |
|
|
| **Demographics.** Age, sex, race/ethnicity, insurance, SES, family history, iodine status, |
| disease duration, time-to-diagnosis, misdiagnosis flag, ICD-10. |
|
|
| **Disorder classification.** Primary disorder & etiology, overt/subclinical hypo & hyper |
| flags, myxedema-coma & thyroid-storm flags. |
|
|
| **HPT-axis hormones.** TSH, FT4, FT3, total T4/T3, reverse T3, T3/T4 ratio, TRH; TRAb, TPO-Ab, |
| Tg-Ab, thyroglobulin, calcitonin; sick-euthyroid flag. |
|
|
| **Hypothyroid symptoms.** Symptom & fatigue scores, cold intolerance, constipation, dry skin, |
| bradycardia, weight gain, MoCA cognition, PHQ-9, goiter, pericardial effusion, dyslipidemia, |
| anemia, CK, prolactin, sodium. |
|
|
| **Hyperthyroid symptoms.** Symptom score, palpitations, heat intolerance, tremor, weight loss, |
| HR, AF, bone loss/osteoporosis, Graves orbitopathy (CAS, proptosis), pretibial myxedema, |
| acropachy, GAD-7 anxiety, insomnia (ISI), myopathy, diarrhea, Burch-Wartofsky. |
|
|
| **Medication & treatment.** Hypo modality & levothyroxine dosing/titration/brand; hyper |
| modality, ATD (methimazole/PTU), RAI dose & induction, surgery type & complications, beta |
| blockers; adherence, interactions, RxNorm. |
|
|
| **Treatment response & QoL.** Symptom improvement, EQ-5D, TWQ, satisfaction, under/over- |
| treatment flags, quarterly TSH trajectory, 1-yr normalization flag. |
|
|
| **Nodule & cancer.** Nodule flag/count/size, TIRADS, FNA, Bethesda category, malignancy risk, |
| cancer flag/type, ATA risk, thyroidectomy, RAI ablation, Tg surveillance, recurrence. |
|
|
| **Comorbidities & labs.** Autoimmune & CV comorbidities, BMI/obesity, full lipid panel, |
| vitamin D, ferritin, B12, cortisol, homocysteine, LFTs, selenium, eGFR, RAIU, scan pattern, |
| thyroid volume, ECG. |
|
|
| **Utilization & pregnancy.** Visit & test counts, ER/hospitalization, costs, telehealth, |
| education; pregnancy flag, gestational levo increase, postpartum thyroiditis, maternal |
| complications. |
|
|
| **Coding standards.** ICD-10, SNOMED, LOINC, RxNorm; FHIR R4 Observation bundle (full product). |
|
|
| --- |
|
|
| ## Files |
|
|
| - `hc_end_004_sample.csv` — 500-patient sample (186 columns) |
| - `generate_sample_dataset_hc_end_004.py` — reproducible generator + validation harness |
| - `validation_report.json` / `validation_report.md` — full scorecard |
| - `sweep_summary.json` — 6-seed determinism results |
|
|
| ## Loading |
|
|
| ```python |
| import pandas as pd |
| df = pd.read_csv("hc_end_004_sample.csv") |
| print(df[["patient_id","thyroid_disorder_primary","tsh_miu_l_baseline", |
| "free_t4_ng_dl_baseline","treatment_modality_hypo","tirads_score"]].head()) |
| ``` |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("csv", data_files="hc_end_004_sample.csv") |
| ``` |
|
|
| ## Use cases |
|
|
| - Thyroid-disorder classification from hormone panels (hypo/hyper/subclinical/autoimmune) |
| - Levothyroxine titration & TSH-normalization trajectory modeling |
| - Graves treatment-pathway and remission/relapse prediction |
| - Thyroid-nodule malignancy-risk modeling (TIRADS → Bethesda → ATA) |
| - Pregnancy thyroid-management and postpartum-thyroiditis tooling |
| - ML training where real thyroid EHR data is PHI-restricted |
|
|
| --- |
|
|
| ## Honest limitations & disclosed generator behavior |
|
|
| This engine is among the better-calibrated XpertSystems SKUs; nonetheless, standard |
| synthetic-data caveats and a few specifics apply: |
|
|
| 1. **Independent symptom draws.** Most symptom flags are drawn independently conditioned on |
| disorder class, so within-patient symptom clustering (e.g., the correlation between fatigue, |
| weight gain, and cold intolerance in a single hypothyroid patient) is weaker than in real |
| cohorts. Disorder-level prevalences are correct. |
| 2. **Quarterly TSH trajectory is monotonic-declining.** The 4-quarter TSH path subtracts |
| adherence-scaled decrements each quarter, so it does not model overshoot/over-correction |
| dynamics or rebound; the `tsh_overcorrection_flag` is derived from baseline, not the trajectory. |
| 3. **Cancer module prevalence is low (~3%).** Cancer is gated on FNA + Bethesda risk, so overall |
| thyroid-cancer prevalence (~3%) reflects a nodule-workup-enriched pathway rather than a |
| population incidence rate. |
| 4. **Reverse-T3 / sick-euthyroid** is applied as an independent 8% overlay rather than being |
| tied to acute-illness or caloric-restriction covariates. |
|
|
| General caveat: cross-field correlations beyond those explicitly modeled may be weaker than in |
| real cohorts. **Not for clinical decision-making** — research/development use only. |
|
|
| --- |
|
|
| ## Commercial product comparison |
|
|
| | Capability | This sample | Full HC-END-004 product | |
| |---|---|---| |
| | Patients | 500 | 20,000+ (configurable) | |
| | Seeds / cohorts | 1 | Multi-seed, reproducible | |
| | Formats | CSV | CSV + Parquet + JSON + **FHIR R4 Bundle** | |
| | Symptom clustering | Independent draws | Correlated within-patient symptom model | |
| | TSH trajectory | Monotonic 4-quarter | Full titration dynamics w/ overshoot | |
| | Cancer incidence | Workup-gated (~3%) | Tunable population-incidence mode | |
| | License | CC-BY-NC-4.0 | Commercial | |
| | Support & SLA | — | Included | |
|
|
| Full product, custom cohorts, or other endocrinology SKUs: **pradeep@xpertsystems.ai** |
|
|
| --- |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{xpertsystems_hc_end_004_2026, |
| title = {HC-END-004: Thyroid Disorders Synthetic Dataset}, |
| author = {XpertSystems.ai}, |
| year = {2026}, |
| publisher = {XpertSystems.ai Synthetic Data Factory}, |
| url = {https://xpertsystems.ai}, |
| note = {Synthetic; CC-BY-NC-4.0 (sample). Calibrated to: NHANES III thyroid |
| prevalence (Hollowell et al. 2002, JCEM); Colorado Thyroid Disease |
| Prevalence Study (Canaris et al. 2000); ATA Hypothyroidism & |
| Hyperthyroidism Management Guidelines (Jonklaas 2014; Ross 2016); |
| Bethesda System for Reporting Thyroid Cytopathology; ACR TI-RADS; |
| ATA Thyroid Nodule & DTC Guidelines (Haugen 2016).} |
| } |
| ``` |
|
|
| *Synthetic data generated by XpertSystems.ai. Not derived from real patient records. |
| Not for clinical use.* |
|
|
