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hcneu006-sample

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metadata 
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<n<10K

HC-NEU-006 — Migraine & Chronic Headache Dataset (Sample)

A schema-identical preview of HC-NEU-006, the XpertSystems.ai synthetic migraine and chronic headache patient cohort dataset for clinical trial research, CGRP-era treatment outcome modeling, ICHD-3 subtype classification ML, AMPP / CaMEO-comparable headache analytics, and migraine-specific machine learning. The full product covers 10,000 patients; this sample is HF-sized at 3,000 patients.

Built by XpertSystems.ai — Synthetic Data Platform Contact pradeep@xpertsystems.ai · 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 

from datasets import load_dataset

ds = load_dataset(
    "xpertsystems/hcneu006-sample",
    data_files="HC_NEU_006_dataset.csv",
    split="train",
)

Or with pandas directly:

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:

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.

XpertSystems.ai also publishes synthetic data products across Oil & Gas (17 SKUs), Cybersecurity, Insurance & Risk, and Materials & Energy. Catalog: huggingface.co/xpertsystems.