HUNTINGTON'S DISEASE ENRICHED COHORT

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 ... Full description with all tables is on the Wiki page of this project. Full dataset and purchase: https://sentineldata.com.ua/dataset/huntington-s-disease-cohort-dataset . Zenodo copy: https://doi.org/10.5281/zenodo.22775393

Authors

Publication Details

Journal
Open Science Framework
Published
2026-11-07
DOI
https://doi.org/10.17605/osf.io/6wn8m
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

HUNTINGTON'S DISEASE ENRICHED COHORT

Chekerskyi Maksym
1 links
Open Science Framework
Machine Learning in Healthcare
article

HUNTINGTON'S DISEASE ENRICHED COHORT

Chekerskyi Maksym
article en

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 ... Full description with all tables is on the Wiki page of this project. Full dataset and purchase: https://sentineldata.com.ua/dataset/huntington-s-disease-cohort-dataset . Zenodo copy: https://doi.org/10.5281/zenodo.22775393

Open Science Framework
Good health and well-being
Openalex Percentile: Top 19%
Machine Learning in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon