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

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

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

14 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 publicationInformation Processing in Medical Imaging - 25th International Conference, IPMI 2017, Proceedings
EditorsHongtu Zhu, Marc Niethammer, Martin Styner, Hongtu Zhu, Dinggang Shen, Pew-Thian Yap, Stephen Aylward, Ipek Oguz
Pages121-133
Number of pages13
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

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10265 LNCS
ISSN0302-9743

Conference

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

Bibliographical note

Funding Information:
This work is part of the EuroPOND initiative, which is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 666992. The authors also thank Dr. Jonathan Huang for sharing the implementation of Huang’s EBM and Dr. Alexandra Young for the useful discussions on estimation of biomarker distributions as well as for sharing the implementation of the simulation system for biomarker evolution.

Publisher Copyright:
© Springer International Publishing AG 2017.

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