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Huntington's disease enriched cohort

CODE SENTINEL DATA, Chekerskyi Maksym

Zenodo (CERN European Organization for Nuclear Research), 2026

5,000,000 participants

Abstract

This Zenodo record contains a free, evaluation-grade sample of 10,000 synthetic patient records — a genuine, unmodified subset of the full production dataset, not a separate re-generation. It is provided so that researchers, ML engineers, and clinical data scientists can inspect the schema, phenotypic depth, and biomarker architecture before licensing the complete asset. The full commercial dataset — 5,000,000 synthetic patients, 11-year longitudinal depth (2016–2026), complete multi-ethnic population stratification, and the full pedigree network — is a licensed product and is not included in this record. Full dataset & enterprise licensing: https://sentineldata.com.ua/dataset/huntington-s-disease-cohort-dataset Abstract This dataset is a synthetic, privacy-safe electronic health record (EHR) cohort standardized to the OMOP Common Data Model (CDM) v5.4, built to address a persistent problem in rare-disease machine learning: the near-total absence of large, structurally complete, freely shareable datasets for orphan neurodegenerative conditions. The cohort is enriched around Huntington's Disease (HD), combining longitudinal clinical trajectories, genetic biomarker assertions (CAG repeat length), multi-generational pedigree graphs, and a realistic comorbidity background. No real patient data of any kind is contained in this file — every record is algorithmically generated. Clinical & Scientific Background Huntington's disease is an autosomal-dominant neurodegenerative disorder…

How strong is the evidence?

Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 5,000,000 participants or records.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

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