{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T04:27:51Z","timestamp":1782361671374,"version":"3.54.5"},"reference-count":58,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer\u2019s disease (AD) and Huntington\u2019s disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80% with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.<\/jats:p>","DOI":"10.1162\/imag_a_00010","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:34:00Z","timestamp":1691742840000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":16,"title":["The temporal event-based model: Learning event timelines in progressive\n          diseases"],"prefix":"10.1162","volume":"1","author":[{"given":"Peter A.","family":"Wijeratne","sequence":"first","affiliation":[{"name":"UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom"},{"name":"Department of Informatics, University of Sussex, Brighton, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arman","family":"Eshaghi","sequence":"additional","affiliation":[{"name":"Queen Square Multiple Sclerosis Centre, 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