{"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/disease-progression-timeline-estimation-for","title":"Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling","arxiv_id":"1808.03604","date":"2018-08-10","proceeding":null,"authors":["Vikram Venkatraghavan","Esther E. Bron","Wiro J. Niessen","Stefan Klein","for the Alzheimer's Disease Neuroimaging Initiative"],"abstract":"Alzheimer's Disease (AD) is characterized by a cascade of biomarkers becoming\nabnormal, the pathophysiology of which is very complex and largely unknown.\nEvent-based modeling (EBM) is a data-driven technique to estimate the sequence\nin which biomarkers for a disease become abnormal based on cross-sectional\ndata. It can help in understanding the dynamics of disease progression and\nfacilitate early diagnosis and prognosis. In this work we propose a novel\ndiscriminative approach to EBM, which is shown to be more accurate than\nexisting state-of-the-art EBM methods. The method first estimates for each\nsubject an approximate ordering of events. Subsequently, the central ordering\nover all subjects is estimated by fitting a generalized Mallows model to these\napproximate subject-specific orderings. We also introduce the concept of\nrelative distance between events which helps in creating a disease progression\ntimeline. Subsequently, we propose a method to stage subjects by placing them\non the estimated disease progression timeline. We evaluated the proposed method\non Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the\nresults with existing state-of-the-art EBM methods. We also performed extensive\nexperiments on synthetic data simulating the progression of Alzheimer's\ndisease. The event orderings obtained on ADNI data seem plausible and are in\nagreement with the current understanding of progression of AD. The proposed\npatient staging algorithm performed consistently better than that of\nstate-of-the-art EBM methods. Event orderings obtained in simulation\nexperiments were more accurate than those of other EBM methods and the\nestimated disease progression timeline was observed to correlate with the\ntimeline of actual disease progression. The results of these experiments are\nencouraging and suggest that discriminative EBM is a promising approach to\ndisease progression modeling.","url_abs":"http://arxiv.org/abs/1808.03604v1","url_pdf":"http://arxiv.org/pdf/1808.03604v1.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":"disease-progression-timeline-estimation-for","repo_url":"https://github.com/88vikram/pyebm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"prognosis","task_name":"Prognosis"}],"methods":[{"method_slug":"ebm","method_name":"EBM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}