--- 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.*