{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-discriminative-event-based-model-for","title":"A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling","arxiv_id":"1702.06408","date":"2017-02-21","proceeding":null,"authors":["Vikram Venkatraghavan","Esther Bron","Wiro Niessen","Stefan Klein"],"abstract":"The event-based model (EBM) for data-driven disease progression modeling\nestimates the sequence in which biomarkers for a disease become abnormal. This\nhelps in understanding the dynamics of disease progression and facilitates\nearly diagnosis by staging patients on a disease progression timeline. Existing\nEBM methods are all generative in nature. In this work we propose a novel\ndiscriminative approach to EBM, which is shown to be more accurate as well as\ncomputationally more efficient than existing state-of-the art EBM methods. The\nmethod first estimates for each subject an approximate ordering of events, by\nranking the posterior probabilities of individual biomarkers being abnormal.\nSubsequently, the central ordering over all subjects is estimated by fitting a\ngeneralized Mallows model to these approximate subject-specific orderings based\non a novel probabilistic Kendall's Tau distance. To evaluate the accuracy, we\nperformed extensive experiments on synthetic data simulating the progression of\nAlzheimer's disease. Subsequently, the method was applied to the Alzheimer's\nDisease Neuroimaging Initiative (ADNI) data to estimate the central event\nordering in the dataset. The experiments benchmark the accuracy of the new\nmodel under various conditions and compare it with existing state-of-the-art\nEBM methods. The results indicate that discriminative EBM could be a simple and\nelegant approach to disease progression modeling.","url_abs":"http://arxiv.org/abs/1702.06408v1","url_pdf":"http://arxiv.org/pdf/1702.06408v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-discriminative-event-based-model-for","repo_url":"https://github.com/88vikram/pyebm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"ebm","method_name":"EBM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}