{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:26:42Z","timestamp":1787981202140,"version":"build-2784847793"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100005725","name":"CHDI Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disorders"],"published-print":{"date-parts":[[2022,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Applying machine\u2010learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT\u2010HD, REGISTRY, TRACK\u2010HD, and Enroll\u2010HD) were integrated and machine\u2010learning methods (Bayesian latent\u2010variable analysis and continuous\u2010time hidden Markov models) were applied to develop a probabilistic model of disease progression. The model was validated using a separate Enroll\u2010HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG\u2010age product.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4\u2009years) of whom 61.5% had manifest disease. Analysis of transition times showed that \u201cearly\u2010disease\u201d states 1 and 2, which occur before motor diagnosis, lasted ~16\u2009years. Increasing numbers of participants had motor onset during \u201ctransition\u201d states 3 to 5, which collectively lasted ~10\u2009years, and the \u201clate\u2010disease\u201d states 6 to 9 also lasted ~10\u2009years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. \u00a9 2021 International Parkinson and Movement Disorder Society<\/jats:p><\/jats:sec>","DOI":"10.1002\/mds.28866","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T07:24:25Z","timestamp":1638775465000},"page":"553-562","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["A Machine\u2010Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design"],"prefix":"10.1002","volume":"37","author":[{"given":"Amrita","family":"Mohan","sequence":"first","affiliation":[{"name":"Center for Computational Health CHDI Management\/CHDI Foundation  Yorktown Heights New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaonan","family":"Sun","sequence":"additional","affiliation":[{"name":"IBM Research  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumya","family":"Ghosh","sequence":"additional","affiliation":[{"name":"IBM Research  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"IBM Research  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swati","family":"Sathe","sequence":"additional","affiliation":[{"name":"Center for Computational Health CHDI Management\/CHDI Foundation  Yorktown Heights New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Hu","sequence":"additional","affiliation":[{"name":"IBM Research  Princeton New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-9079","authenticated-orcid":false,"given":"Cristina","family":"Sampaio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2015.5"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.26011"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/mds.20332"},{"key":"e_1_2_8_5_1","unstructured":"Cooperative Huntington's Observational Research Trial (NCT00313495).https:\/\/clinicaltrials.gov\/ct2\/show\/NCT00313495. 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