A discriminative event based model for Alzheimer’s disease progression modeling

Vikram Venkatraghavan, Esther E. Bron, Wiro J. Niessen, Stefan Klein

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    7 Citations (Scopus)

    Abstract

    The event-based model (EBM) for data-driven disease progression modeling estimates the sequence in which biomarkers for a disease become abnormal. This helps in understanding the dynamics of disease progression and facilitates early diagnosis by staging patients on a disease progression timeline. Existing EBM methods are all generative in nature. In this work we propose a novel discriminative approach to EBM, which is shown to be more accurate as well as computationally more efficient than existing state-of-the art EBM methods. The method first estimates for each subject an approximate ordering of events, by ranking the posterior probabilities of individual biomarkers being abnormal. Subsequently, the central ordering over all subjects is estimated by fitting a generalized Mallows model to these approximate subject-specific orderings based on a novel probabilistic Kendall’s Tau distance. To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer’s disease. Subsequently, the method was applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data to estimate the central event ordering in the dataset. The experiments benchmark the accuracy of the new model under various conditions and compare it with existing state-of-the-art EBM methods. The results indicate that discriminative EBM could be a simple and elegant approach to disease progression modeling.

    Original languageEnglish
    Title of host publicationProceedings of 25th International Conference, IPMI 2017
    PublisherSpringer
    Pages121-133
    Volume10265 LNCS
    ISBN (Print)9783319590493
    DOIs
    Publication statusPublished - 2017
    Event25th International Conference on Information Processing in Medical Imaging, IPMI 2017 - Boone, United States
    Duration: 25 Jun 201730 Jun 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10265 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Conference

    Conference25th International Conference on Information Processing in Medical Imaging, IPMI 2017
    CountryUnited States
    CityBoone
    Period25/06/1730/06/17

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