Unified modeling of imputation, forecasting, and prediction for AD progression

Wonsik Jung, Ahmad Wisnu Mulyadi, Heung Il Suk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a novel deep recurrent neural network as an Alzheimer’s Disease (AD) progression model, capable of jointly conducting tasks of missing values imputation, phenotypic measurements forecast, and clinical state prediction of a subject based on his/her longitudinal imaging biomarkers. Unlike the existing methods that mostly ignore missing values or impute them by means of an independent statistical model before training a disease progression model, we devise a unified recurrent network architecture for jointly performing missing values imputation, biomarker values forecast, and clinical state prediction from the longitudinal data. For these tasks to be handled in a unified framework, we also define an objective function that can be efficiently optimized by means of stochastic gradient descent in an end-to-end manner. We validated the effectiveness of our proposed method by comparing with the comparative methods over the TADPOLE challenge cohort.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
PublisherSpringer
Pages168-176
Number of pages9
ISBN (Print)9783030322502
DOIs
Publication statusPublished - 2019 Jan 1
Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 2019 Oct 132019 Oct 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11767 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period19/10/1319/10/17

Fingerprint

Alzheimer's Disease
Missing Values
Imputation
Biomarkers
Progression
Forecasting
Forecast
Recurrent neural networks
Prediction
Network architecture
Modeling
Recurrent Networks
Stochastic Gradient
Gradient Descent
Recurrent Neural Networks
Longitudinal Data
Network Architecture
Imaging techniques
Statistical Model
Objective function

Keywords

  • Alzheimer’s Disease
  • Deep learning
  • Disease Progression Modeling
  • Longitudinal data
  • Mild Cognitive Impairment
  • Missing value imputation
  • Recurrent neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Jung, W., Mulyadi, A. W., & Suk, H. I. (2019). Unified modeling of imputation, forecasting, and prediction for AD progression. In D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, ... S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 168-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer. https://doi.org/10.1007/978-3-030-32251-9_19

Unified modeling of imputation, forecasting, and prediction for AD progression. / Jung, Wonsik; Mulyadi, Ahmad Wisnu; Suk, Heung Il.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer, 2019. p. 168-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Jung, W, Mulyadi, AW & Suk, HI 2019, Unified modeling of imputation, forecasting, and prediction for AD progression. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11767 LNCS, Springer, pp. 168-176, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/13. https://doi.org/10.1007/978-3-030-32251-9_19
Jung W, Mulyadi AW, Suk HI. Unified modeling of imputation, forecasting, and prediction for AD progression. In Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer. 2019. p. 168-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-32251-9_19
Jung, Wonsik ; Mulyadi, Ahmad Wisnu ; Suk, Heung Il. / Unified modeling of imputation, forecasting, and prediction for AD progression. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer, 2019. pp. 168-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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