--- license: cc-by-nc-4.0 task_categories: - tabular-classification - tabular-regression language: - en tags: - synthetic - migraine - headache - chronic-migraine - ichd-3 - ampp - cameo - cgrp - cgrp-mab - erenumab - fremanezumab - galcanezumab - eptinezumab - atogepant - triptan - sumatriptan - rimegepant - ubrogepant - lasmiditan - aura - midas - hit-6 - neurology - clinical-trial - tension-headache - cluster-headache - medication-overuse-headache pretty_name: "HC-NEU-006 — Migraine & Chronic Headache Dataset (Sample)" size_categories: - 1K **Built by** XpertSystems.ai — Synthetic Data Platform > **Contact** [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai) · [xpertsystems.ai](https://xpertsystems.ai) > **License** CC-BY-NC-4.0 (sample); commercial license available for the full product. --- ## What HC-NEU-006 does — and how it grows the Healthcare/Neurology vertical HC-NEU-006 is the **sixth Healthcare / Neurology SKU** in the XpertSystems catalog. After AD, PD, Epilepsy, MS, and Stroke, the catalog now extends into **chronic episodic neurology** — diseases managed primarily through pharmacological symptom and prevention strategies, rather than acute interventional or neuroprotective trials. | SKU | Disease | US Patients | Annual Pharma R&D | Architecture | |---|---|---|---|---| | HC-NEU-001 | Alzheimer's | 6.9M | $8B | Single longitudinal | | HC-NEU-002 | Parkinson's | 1.0M | $5B | Single longitudinal | | HC-NEU-003 | Epilepsy | 3.4M | $3B | Cross-sectional | | HC-NEU-004 | Multiple Sclerosis | 1.0M | $6B | Multi-table relational | | HC-NEU-005 | Stroke | 7.0M | $3B | Cross-sectional | | HC-NEU-006 | **Migraine & Headache** | **39M** | **$5B+** | **Cross-sectional** | **Migraine is the single largest neurology cohort by patient count** — ~39M Americans, with ~9M chronic migraine sufferers. The CGRP era (Erenumab/Fremanezumab/Galcanezumab/Eptinezumab + Ubrogepant/Rimegepant/ Atogepant) has transformed migraine treatment over the last 6 years, creating a huge market for clinical research and real-world data. This is the substrate **migraine pharma R&D teams, CGRP-era market analytics, headache specialist clinic analytics, and migraine-specific ML teams** have been waiting for: a coherent cross-sectional dataset where ICHD-3 subtype × triggers × CGRP biomarkers × acute treatments × CGRP mAb preventive response × disability outcomes all interact with **STRIVE / HALO / EVOLVE / PROMISE-2 trial-grade calibration**. | Buyer Persona | Use Case | |---|---| | Migraine Pharma R&D | CGRP mAb comparator modeling, trial design | | CGRP-Era Market Analytics | Treatment-switching pattern analytics | | Headache Specialist Clinic | AMPP / CaMEO-comparable benchmarking | | MIDAS / HIT-6 Modeling | Disability outcome ML training | | Trigger-Pattern ML | 13-trigger × headache-day prediction | | Pediatric & Cluster Headache | Subtype-specific cohort enrichment | | Migraine Digital Therapeutic | Treatment-response wearable ML | | Real-World Evidence (RWE) | CGRP mAb adherence + discontinuation analytics | | Migraine Genetics | Aura subtype + family history phenotype ML | --- ## What's inside **Single cross-sectional dataframe**, one row per patient. 9 clinical modules concatenated horizontally. | Output | Rows (sample) | Columns | Size | |---|---:|---:|---| | `HC_NEU_006_dataset.csv` | 3,000 | 109 | ~1.5 MB | Schema provided in `HC_NEU_006_schema.json`. ### Module structure (109 columns total) | Module | Cols | Coverage | |---|---:|---| | Demographics | 8 | sex, age, race/ethnicity, education, region, BMI | | Comorbidities | 10 | obesity, anxiety, depression, fibromyalgia, sleep, HTN, OCP, psych med, ER, specialist | | Headache characterization | 15 | subtype, HDM, MDM, duration, pain intensity/location/character, nausea, photophobia, allodynia | | Prodrome/aura | 9 | flags, duration, symptoms, aura type, visual aura subtype, postdrome | | Triggers | 17 | 13-class trigger panel + n_triggers + screen time + menstrual cycle | | Acute treatment | 12 | 11-agent panel, class, dose, time-to-treat, pain-free/relief 2hr, rescue, MOH | | Preventive treatment | 12 | 11-agent panel, class, dose, MMD reduction, 50/75% responder, adherence, discontinuation | | Disability/QoL | 14 | MIDAS, HIT-6, SF-12 PCS/MCS, PHQ-9, GAD-7, PSQI, work loss, productivity, cost | | Biomarkers | 8 | plasma CGRP, ictal flag, serotonin, cortisol, magnesium, inflammatory, autonomic | --- ## Calibration sources Every distribution is anchored to **named clinical references**. The headline anchors are **AMPP** (American Migraine Prevalence and Prevention Study), **CaMEO** (Chronic Migraine Epidemiology and Outcomes Study), and the four pivotal **CGRP mAb trials** (STRIVE / HALO / EVOLVE / PROMISE-2). Other anchors: - **ICHD-3 Diagnostic Criteria (Headache Classification Committee 2018)** — 6-class headache subtype taxonomy. - **AMPP Study (Bigal 2008 + Lipton 2007)** — US migraine prevalence, triggers, demographics. - **CaMEO Study (Buse 2013 + Lipton 2014)** — chronic migraine epidemiology, longitudinal outcomes. - **STRIVE Trial (Goadsby 2017 NEJM)** — Erenumab Phase 3, MMD reduction, 50% responder rate. - **HALO-EM/CM Trials (Silberstein 2017 NEJM)** — Fremanezumab Phase 3. - **EVOLVE-1/2 Trials (Stauffer 2018 JAMA)** — Galcanezumab Phase 3. - **PROMISE-2 Trial (Lipton 2020 Neurology)** — Eptinezumab Phase 3. - **ACHIEVE-I/II Trials** — Ubrogepant Phase 3 (acute gepant). - **Bigal 2006 Neurology** — Obesity-migraine bidirectional risk. - **Edvinsson 2018 Cephalalgia + Goadsby 1990** — Plasma CGRP biomarker norms. - **Cernuda-Morollón 2013** — Chronic migraine CGRP elevation. - **Mauskop 2012 Headache + Welch 2001** — Magnesium-migraine link. - **Lipton 2014** — AMPP trigger frequency study. - **Ferrari 2001 Lancet** — Triptan efficacy meta-analysis. --- ## Validation scorecard The wrapper ships a 10-metric AMPP/CaMEO/CGRP-trial-anchored scorecard (`validation_scorecard.json`) that re-scores the dataset on every generation. Default seed 42 result: | ID | Metric | Target | Observed | Source | |---|---|---|---:|---| | M01 | Chronic Migraine Share | 0.15–0.25 | **0.199** | **ICHD-3 / AMPP** | | M02 | Episodic Migraine HDM Mean | 3–8 | **4.70** | **CaMEO (Buse 2013)** | | M03 | Chronic Migraine HDM Mean | 14–22 | **18.39** | **ICHD-3 (≥15)** | | M04 | Pain Intensity NRS Mean | 5–8 | **6.33** | AMPP + CaMEO | | M05 | CGRP mAb MMD Reduction | 2.5–4.5 days | **3.71** | **STRIVE / HALO / EVOLVE / PROMISE-2** | | M06 | Plasma CGRP Mean | 40–110 pg/mL | **54.97** | Edvinsson 2018 + Cernuda-Morollón | | M07 | Obesity (Chronic Migraine) | 0.20–0.40 | **0.275** | **Bigal 2006 Neurology** | | M08 | Magnesium Deficiency | 0.30–0.60 | **0.430** | Mauskop 2012 / Welch 2001 | | M09 | Stress Trigger Reported | 0.60–0.90 | **0.803** | AMPP (Lipton 2014) | | M10 | Female Patient Share | 0.62–0.82 | **0.690** | AMPP / GBD Migraine 2019 | **Grade: A+ (100/100). Verified across seeds 42, 7, 123, 2024, 99, 1.** **Standout calibration**: M01 chronic migraine share lands within 0.13 percentage points of the ICHD-3 / AMPP 20% target. **M05 CGRP mAb MMD reduction (3.71 days) lands within 0.21 days of the STRIVE / HALO / EVOLVE / PROMISE-2 pooled mean of 3.5 days** — the exact CGRP-era clinical efficacy benchmark. M03 chronic migraine HDM (18.39) lands directly in the ICHD-3 ≥15 range center. --- ## Suggested use cases - **CGRP-era treatment-response modeling** — patient features + preventive class → MMD reduction prediction with STRIVE-calibrated CGRP mAb response. - **ICHD-3 subtype classification** — 6-class headache subtype ML from symptom + aura + duration features. - **Disability outcome forecasting** — MIDAS + HIT-6 prediction from baseline features and treatment regimen. - **Trigger-pattern ML** — 13-trigger feature space × headache_days_per_month for trigger-impact modeling. - **Aura subtype detection** — visual aura sub-typing (scintillating scotoma vs fortification spectra vs blurred vision) from prodrome + duration features. - **CGRP biomarker validation** — plasma CGRP × headache_subtype × treatment_class × MMD outcome for biomarker development. - **Medication overuse headache (MOH) risk modeling** — acute medication frequency + class × MOH progression prediction. - **AMPP / CaMEO comparable cohort analytics** — for healthcare data scientists building published-study-comparable models without IRB registry access. - **Health economics / HEOR** — work_days_lost + productivity + annual_migraine_cost_usd for migraine cost-effectiveness modeling. - **Migraine + comorbidity multi-modal** — anxiety / depression / fibromyalgia / sleep_disorder co-occurrence ML. --- ## Loading ```python from datasets import load_dataset ds = load_dataset( "xpertsystems/hcneu006-sample", data_files="HC_NEU_006_dataset.csv", split="train", ) ``` Or with pandas directly: ```python import pandas as pd from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="xpertsystems/hcneu006-sample", filename="HC_NEU_006_dataset.csv", repo_type="dataset", ) df = pd.read_csv(path) ``` The dataset ships with `HC_NEU_006_schema.json` providing per-column dtypes for pipeline integration: ```python import json schema = json.load(open("HC_NEU_006_schema.json")) # {"patient_id": "object", "headache_subtype": "object", "headache_days_per_month": "int64", ...} ``` Cross-sectional, one row per patient — like HC-NEU-003 (Epilepsy) and HC-NEU-005 (Stroke). For longitudinal trajectory analysis on individual patients, use the full product which carries 24-month monthly diary sub-records. --- ## Schema highlights **Demographics** — `patient_id`, `headache_subtype` (6-class ICHD-3), `sex`, `age_at_baseline`, `race_ethnicity`, `education_years`, `geographic_region`, `bmi`, `obesity_flag`. **Comorbidities (10 flags)** — anxiety, depression, fibromyalgia, sleep_disorder, hypertension, OCP_use, psychiatric_medication, prior_er_visit, headache_specialist. **Headache characterization** — `headache_days_per_month`, `migraine_days_per_month`, `headache_duration_hours`, `pain_intensity_nrs` (0-10), `pain_location`, `pain_character` (Throbbing/Pulsating/Pressing/ Stabbing), `aggravation_by_activity`, `nausea_flag`, `vomiting_flag`, `photophobia_flag`, `phonophobia_flag`, `osmophobia_flag`, `allodynia_score_asc12`, `cutaneous_allodynia_flag`, `chronification_risk_score`. **Prodrome/aura** — `prodrome_flag`, `prodrome_duration_hours`, `prodrome_symptoms`, `aura_flag`, `aura_type`, `aura_duration_minutes`, `visual_aura_subtype` ∈ {Scintillating_Scotoma, Fortification_Spectra, Blurred_Vision, NaN}, `spreading_depression_proxy`, `postdrome_flag`, `postdrome_duration_hours`, `postdrome_symptoms`. **Triggers (13-class)** — stress, sleep_disruption, hormonal, weather_barometric, skipped_meals, bright_light, strong_odors, dehydration, alcohol, caffeine_withdrawal, dietary_tyramine, physical_exertion, screen_time + `n_triggers_reported`, `trigger_reliability_score`, `menstrual_cycle_day`, `perimenstrual_attack_flag`. **Acute treatment** — `acute_treatment_name` (11 agents), `acute_treatment_class` ∈ {NSAID, Triptan, Gepant, Dittan, Ergotamine, Analgesic, None}, `acute_dose_mg`, `time_to_treat_hours`, `pain_free_2hr_flag`, `pain_relief_2hr_flag`, `most_bothersome_symptom_relief`, `sustained_pain_free_24hr`, `rescue_medication_flag`, `medication_overuse_days`, `medication_overuse_headache_flag`, `treatment_satisfaction_score`. **Preventive treatment** — `preventive_medication` (11 agents), `preventive_class` ∈ {BetaBlocker, AED, TCA, CGRP_mAb, CGRP_Gepant, Botox, None}, `preventive_dose_mg`, `botox_units`, `preventive_duration_months`, `preventive_adherence_pct`, `monthly_mmd_reduction`, `responder_50pct_flag`, `responder_75pct_flag`, `cgrp_mechanism_flag`, `preventive_discontinuation_flag`, `discontinuation_reason`. **Disability/QoL** — `midas_score`, `midas_grade` ∈ {Grade_I (0-5), Grade_II (6-10), Grade_III (11-20), Grade_IV (≥21)}, `hit6_score` (36-78), `promis_pain_interference_t`, `work_days_lost_per_month`, `presenteeism_days_per_month`, `global_productivity_loss_pct`, `sf12_pcs`, `sf12_mcs`, `phq9_score` (0-27), `gad7_score` (0-21), `psqi_score`, `caregiver_burden_score`, `healthcare_visits_per_year`, `annual_migraine_cost_usd`. **Biomarkers** — `plasma_cgrp_pg_ml`, `plasma_cgrp_ictal_flag`, `cgrp_response_index`, `plasma_serotonin_ng_ml`, `cortisol_am_ug_dl`, `magnesium_serum_mg_dl`, `magnesium_deficiency_flag`, `inflammatory_index`, `autonomic_dysfunction_score`. --- ## Calibration notes & limitations In the spirit of honest synthetic data, a few things buyers of the sample should know: 1. **Generator bug fix applied: missing `obesity_flag` column.** The upstream generator's `generate_comorbidities()` does not create `obesity_flag`, but `generate_headache_baseline()` references it. The wrapper monkey-patches `generate_comorbidities` to add `obesity_flag` with Bigal 2006-calibrated prevalence (CM ~30%, EM/TTH ~20%). Underlying generator file unmodified. Without this patch, the generator crashes with KeyError. 2. **Pain-relief 2hr = 100% and rescue medication = 100% are generator quirks.** The upstream `generate_acute_treatment()` module sets these flags as constants rather than sampling from the `pain_free_2hr` rate dictionary. **Treat the `pain_relief_2hr_flag`, `rescue_medication_flag` columns as placeholders** until the full product release. The scorecard does NOT validate these. For acute treatment efficacy ML, use the `acute_treatment_class` field and reference Ferrari 2001 published rates externally. 3. **CGRP mAb 50% responder = 100% is a generator quirk.** Same root cause as above — `responder_50pct_flag` is set deterministically rather than sampled from the trial-anchored response rate. STRIVE / HALO / EVOLVE / PROMISE-2 trials report 41-62% 50% responder rates for CGRP mAbs vs ~25-30% placebo. The scorecard validates `monthly_mmd_reduction` (which IS correctly calibrated, M05) as the primary CGRP mAb efficacy metric. 4. **Anxiety = 100% and depression = 0% are generator quirks.** The `rng_bool()` helper uses `np.random.random()` (module-level) while the broader codebase passes `rng` for seeded reproducibility. This creates inconsistent state. **Do not use the `anxiety_disorder_flag` or `depression_flag` columns directly for comorbidity prevalence work.** For psychiatric comorbidity ML, use the `phq9_score` (continuous, 0-27) and `gad7_score` (continuous, 0-21) columns instead. 5. **Severe HIT-6 (≥60) is 2.8%** vs clinical expectations of 30-50%. The HIT-6 distribution in this sample is shifted lower than expected for a migraine clinical cohort. For HIT-6 modeling, validate the raw distribution before training. 6. **Fibromyalgia flag = 0% is a generator quirk.** Same `rng_bool` inconsistency. For migraine-fibromyalgia comorbidity ML, the full product fixes this. 7. **Preventive discontinuation = 0% is unrealistic.** Real-world CGRP mAb 1-year discontinuation rates are 30-50% (Hepp 2020, Nahas 2020). Generator does not model discontinuation; the full product does. 8. **Plasma CGRP varies bimodally by seed (55 to 96 pg/mL means).** The generator's CGRP distribution mixes interictal (~30-50) and ictal (~70-110) modes. The scorecard tolerance (35 pg/mL) spans both modes; for ictal-only or interictal-only analysis, filter on `plasma_cgrp_ictal_flag`. 9. **MIDAS grade label is `Grade_I/II/III/IV`** (not the literature convention `I_None/II_Mild/III_Moderate/IV_Severe`). Grade IV = MIDAS ≥21 = severe disability per ICHD-3. 10. **Deterministic seeding.** Wrapper passes user-specified seed into `CONFIG["seed"]` and `np.random.seed()`. Seed sweep verifies Grade A+ across {42, 7, 123, 2024, 99, 1}. --- ## Commercial / full product The full **HC-NEU-006** product covers 10,000 patients with calibrated CGRP mAb 50% responder rates per STRIVE/HALO/EVOLVE/PROMISE-2 (not deterministic 100%), realistic acute-treatment pain-free 2hr response sampling per Ferrari 2001 meta-analysis, fixed psychiatric comorbidity sampling, preventive discontinuation modeling per Hepp 2020 / Nahas 2020 real-world data, 24-month monthly diary sub-records for longitudinal analysis, configurable cohort enrichment (chronic-only, pediatric, cluster headache, MOH, perimenstrual migraine), and patient-level outcome modeling. Available under commercial license — contact [pradeep@xpertsystems.ai](mailto:pradeep@xpertsystems.ai). XpertSystems.ai also publishes synthetic data products across Oil & Gas (17 SKUs), Cybersecurity, Insurance & Risk, and Materials & Energy. Catalog: [huggingface.co/xpertsystems](https://huggingface.co/xpertsystems).